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1. Introduction

Productivity management in companies has become increasingly crucial in an environment of external openness and globalization of business. Without knowing their productivity, it is unlikely that a company will be successful or even survive in the market [1]. Ishizaka et al. [2] stated that, considering the current increasingly globalized context and the huge amount of data present in the daily life of a company, decision-makers have more than ever the need to manage their business efficiently and productively. For this reason, the theme has been discussed in works from different contexts, such as academy [3,4], computing [5], public services [6], economics [7,8], forest conservation [9], and software development teams [10,11,12,13,14].

In the context of software development, productivity is related to the concepts of efficiency and effectiveness of development processes [15]. Morasca and Russo [16] stated that productivity can be an item in the quality process. Therefore, the increase in productivity is one of the motivating sources of the process improvement programs [17]. Mizuno et al. [18] mentioned that, in a software organization, improving productivity consequently also improves issues related to deadlines, costs, and user satisfaction. Still, in the same context, Ramírez-Mora and Oktaba [19] stated that one of the main objectives in software development is to improve productivity, as organizations want to produce more software and, at the same time, reduce development costs.

Fardo [20] stated that a team’s productivity is related to how motivated its members are. The feeling of motivation is one of the consequences of the proper application of the concept of gamification, which we can understand as the use of elements characteristic of game mechanics outside this context as a way of solving problems and motivating a specific target audience [21].

When analyzing the literature, it is possible to see several definitions for the term productivity and factors described as influential on it [5,19,22]. According to Tangen [23], there is no consensus in the industry regarding what productivity means. Therefore, in this work, we used the concept for productivity presented by Sadowski et al. [5], which states that there is no single metric or factor that defines productivity but a set of factors.

Given the context described, we developed a web system to assist organizations and development teams in measuring productivity in software development. This system uses the influencing factors identified in the literature as parameters to perform the measurement and uses the benefits of gamification to motivate participants in the measurement process.

This research was carried out in four stages: 1. The first stage consisted of a literature review to identify the factors that influence productivity. 2. In this stage, we applied a questionnaire where software development teams evaluated these factors. The developed system used the result of the questionnaire to weigh a factor when measuring productivity. We analyzed the target audience characteristics to find appropriate gamification techniques to motivate the system users. From that, we defined the gamification project. 3. The third step consisted of developing the application. 4. Lastly, a case study was planned and implemented.

We organized this work as follows. Section 2 contains a contextualization of the related subjects to this research. Section 3 describes the research methodology and details the processes used in the theoretical study, planning, construction, and case study. Section 4 presents the details of the developed system as well as the result of its use. Section 5 shows some threats to the validity of this work. Lastly, Section 6 presents the conclusions and future work.

2. Background

2.1. Productivity

The existing competitiveness in the software production industry requires shorter delivery times for the products produced and, in such a way, creates the need to increase the teams’ performance so that companies can remain in the market [22]. In addition, the number of demands related to software development has grown faster than the number of new professionals, and for this reason, optimizing the productivity of existing professionals is of paramount importance [24]. Kemerer [24] stated that, in this context, improving productivity means producing the same systems with a reduced number of resources. In addition to that, Ramírez-Mora and Oktaba [19] stated that one of the main goals in development is to improve productivity as organizations want to produce more software and, at the same time, reduce development costs.

In the context of software development, Graziotin and Fagerholm [25] pointed out that the link between happiness and productivity is real and claimed that the factors that make team members most unhappy are low cognitive performance, mental malaise, low motivation, withdrawal/denial of work, delays, procedural deviations, low code quality, destruction of produced code, flow break, and low productivity. The authors concluded that lower productivity causes unhappiness and that unhappiness causes low productivity.

We can also describe productivity in software development by the relationship between the size of the software delivered (input from the development process) and the effort spent in building the software (output from the development process). Therefore, the general productivity equation is given by productivity = size/effort [12,26,27]. As stated by Delaney and Schmidt [28], such a definition cannot be applied universally. On the other hand, Sadowski et al. [5] stated that we see productivity through different lenses:

Stakeholders: different stakeholders can value a different set of characteristics and objectives;
Context: the particularities of the project and the social and cultural characteristics of the context can change the perception of productivity. For example, if developers feel that helping others is an action valued by the team, they will feel that spending time helping others is productive;
Level: individuals, teams, organizations, and communities have different perceptions of productivity. For example, under the view of a developer, refactoring a module of a system that is already working can be considered productive and can be understood as unproductive by other team members;
Period: a process change can slow down the development in the present but lead to improved team learning over time. Likewise, short-term speed improvements can lead to fatigue and less developer satisfaction over a long period.

In this work, we use the concept for productivity presented by Sadowski et al. [5]. The authors stated in their work (which aimed to develop a framework to conceptualize productivity in software development) that there is no metric or factor that defines productivity precisely but rather a set of factors. In that work, they considered only three factors: speed, quality, and satisfaction.

2.2. Gamification

According to Schlemmer [21], gamification consists of using elements characteristic of game mechanics outside of such games as a way of solving problems and motivating a specific target audience. Navarro [29], in a similar definition, stated that it is the application of elements, mechanisms, dynamics, and techniques of games in a context external to the games, that is, in the individual’s professional, school, or social life. Fardo [20] emphasized that gamification does not imply creating a game that addresses the problem.

According to Zichermann and Cunningham [30], the main idea behind a gamified system is that the user can use intrinsic (such as competition and cooperation) and external stimuli (such as points, levels, missions, and rankings) to carry out the proposed tasks. We can perceive the possibility of using gamification in varied contexts because there are currently several works and publications related to gamified processes involving areas such as physics, math, pedagogy, biology, languages, story, and chemistry [31].

Concerning ways of applying gamification, the Octalysis framework is widely used. This framework aims to facilitate the gamification of processes through the analysis of four aspects that influence people: additives (by providing rewards and providing pleasant feelings), subtractive (by providing motivation generated by the fear that something will happen), intrinsic (elements internal to the individual that influence his motivation, such as self-realization and self-esteem), and extrinsic (external to the individual that affects his motivation) [32]. Chou [32] presented Octalysis divided into eight core drives aimed at motivating people:

C1—Epic Meaning and Calling: seeks to give the person the conviction that he/she is doing something greater than themself or has been chosen to take action.
C2—Development and Accomplishment represents the internal impulse of the human being to progress, develop skills, achieve mastery and overcome challenges.
C3—Empowerment of Creativity and Feedback: this happens when users are engaged in a creative process where new things or new combinations are constantly being discovered.
C4—Social Influence and Relatedness: this core drive contains the social elements that motivate people, such as orientation, social acceptance, social feedback, companionship, competition, envy.
C5—Unpredictability and Curiosity: niches of people may feel more engaged by participating in unpredictable processes that stimulate their curiosity.
C6—Loss and Avoidance are the core drive that describes the motivation generated by the will to avoid a negative event.
C7—Scarcity and Impatience: this drive represents the motivation obtained by wanting something that you cannot easily have.
C8–=Ownership and Possession represents the motivation obtained by owning something.

It is important to note that the effect of these core drives can be amplified or diminished according to the public profile of the gamified process. Therefore, when applying gamification, it is essential to know the target audience [32].

2.3. Related Works

The literature presents several works that, regardless of having this as their goal, report factors that influence the productivity of individuals or teams. Among these factors, the one with the most influence on the present work is that of Canedo and Santos [22], who described the factors that influence productivity in four groups (people, product, organization, and free software projects), containing a list of thirty-seven factors that influence productivity. In this same context, Oliveira et al. [14] conducted an empirical study that concluded that the most used metrics for measuring productivity are time, effort, and the number of lines of code.

Oliveira et al. [33] researched whether productivity metrics can complement team leaders’ perceptions of the topic. During their work, Oliveira et al. [33] classified metrics into two groups: code-based metrics (basically the amount of code produced) and commit-based metrics (commit activity). Their results show that the perception of leaders is generally more correlated with code-based metrics than commit-based metrics. Finally, they concluded that obtaining data on productivity metrics coupled with the perceptions of leaders can "strengthen the organization’s conviction about productive developers and can reveal productive developers not yet perceived by team leaders”.

In this work, we use the same organization of the four groups of factors presented by Canedo and Santos [22]. We also add to the list of factors other works, such as the ones developed by Graziotin and Fagerholm [25], Souza et al. [34], Murphy et al. [35], Sadowski et al. [5], and Ramírez-Mora e Oktaba [19], among others.

The literature contains several works related to gamification in the Information and Communication Technology (ICT) field. Among them, we can mention the work of Naik and Jenkins [36] that described a gamified process called "Relax, It’s a Game", which aims to teach university students the SCRUM agile development methodology. They aim for that in a simplified, more interesting, and less pressing way when compared with traditional teaching methods.

Considering Schlemmer’s statement [21] that one of the goals of gamification is to motivate the participant in the gamified process and associating this assertion with the fact that the productivity of a team is related to their motivation [37], the use of gamification can have a positive impact on productivity.

Reinforcing that, Moldon et al. [38] conducted a study where they examined how software developers’ behavior changes in response to the removal of gamification elements from GitHub. According to the authors, the change triggered significant changes in behavior. For example, long-lasting activity sequences (streaks) have been dropped and become less frequent, weekend activities have diminished, and days with developers’ contributions have become less common. Thus, this work provides evidence of the significant impact of gamification on the behavior of software developers.

In the context of gamification and productivity, Coonradt and Nelson [39] analyzed the fact that, in sports, people try harder than at work. The authors stated that the participant rarely knows the result or what they need to do to win. In addition, the authors say that recreational activities applied to the workplace promote increased productivity. The association between gamification and productivity is natural since some techniques related to gamification (such as rankings, scores, levels, and challenges) are motivational factors for productivity when used in software development [40]. A ranking, for example, when comparing the position of the players, motivates such players to be better and better (to produce more and more) [40]. In this paper, we use gamification in the implementation of the web application to engage the members of the development teams in the process of measuring and improving productivity.

Helie et al. [41] carried out work to measure productivity in software development applying machine learning techniques in a version control system. The authors calculated productivity considering three factors: quantity, quality, and size. For Helie et al. [41], the amount of work can be defined by a model that predicts the number of hours spent per change, quality is defined by a model that predicts the distribution of problems identified through a code analysis tool, and the size is inferred by the number of lines of code. The authors did not specify what issues they could point out or which code analysis tool they used. In addition, the authors described only machine learning techniques and did not present the results related to the way of use or its application in a software development context [41].

Sadowski et al. [5] proposed a framework to conceptualize productivity in software development according to three dimensions (speed, quality, and satisfaction) essential for understanding productivity. Their framework presented, therefore, seeks only to conceptualize productivity according to the three dimensions, and, consequently, processes related to its measurement are not part of the scope.

The productivity management process presented by King and Lima [42] used the Analytic Hierarchy Process (AHP), a method to assist in decision making, and through this method, allowing for the prioritization of factors by a decision-maker on a nine-point scale. This process has no pre-established factors and remains in the hands of the decision-maker, including the selection of which factors to use and the decision on how to measure them.

Ciervo el al. [43] introduced a new risk and issue management system to improve productivity, quality, and compliance in clinical trials. The authors used JIRA, a popular issue and project tracking tool for software development, in conjunction with third-party and custom plug-ins to provide the additional functionality lacking in the core product. The new system integrates issues into a single tracking tool and offers a range of features such as configurable issue management workflows, integration with other systems, reporting, and trends in a web interface. Similar to our work, the authors used a system that captures and exposes metrics that, when analyzed, can improve productivity. Unlike our work, the authors’ research does not have the specific objective of measuring productivity. Furthermore, in our work, in addition to exposing metrics, we use gamification to motivate users to use the process.

Palvalin et al. [44] introduced SmartWoW, a tool for measuring job performance in a changing environment. Similar to our proposal, SmartWoW gives its final result by evaluating factors using a scale. In SmartWoW, six factors are considered: physical environment, virtual environment, social environment, individual work practices, well-being at work, and productivity. Although they suggest that the last two factors (well-being at work and productivity) are consequences of the previous ones (physical, virtual and social environments, and individual work practices), its measurement is not an objective of the work. In the case of our tool, productivity measurement is the main focus of the work.

Finally, we mention the work of Balk [45], which presented a toolbox for calculating and decomposing total factor productivity indices called the Total Factor Productivity (TFP) Toolbox. Quantity-only indices (Malmquist and Moorsteen-Bjurek) as well as price-based indices (Fisher and Trnqvist) can be calculated and decomposed. The toolbox relies on mathematical programming techniques to calculate the different indices. Unlike TFP Toolbox, our work does not use such mathematical formulas and chooses to use factors evaluated by team members. Furthermore, in our work, we use gamification to motivate users to use the process.

3. Study Settings

The development of this work was carried out according to the following steps: theoretical study, planning, construction, and case study, as shown in Figure 1. The following research questions were defined:

RQ.1: What is the effect on productivity assessments of adding gamification?

RQ.2: What is the perception of users concerning the productivity calculation system?

3.1. Theoretical Study

The first stage consisted of a literature review. The objective of this stage was that the context related to the research was better known, and for this reason, this was the first stage of the work.

During the theoretical study stage, we used the following sources of knowledge: articles, books, dissertations, theses, technical manuals, and web pages found from the literature review. The themes studied during this stage were productivity and gamification. To identify the factors that influence productivity, we conducted a complementary literature review on the Systematic Literature Review (SLR) carried by Canedo and Santos [22].

The main output for the next steps was the list of factors influencing productivity. This list was used extensively in later steps and is presented in Figure 2.

3.2. Planning

After carrying out the theoretical study, we started the planning phase, where we defined the identified factors, the gamification project, and the functionalities of the web system.

3.2.1. Identified Factors

We identified a total of seventy-five factors that influence productivity, as shown in Figure 2. The development teams assessed these factors through a survey. In total, forty-four participants rated their agreement to the factors identified in the literature, using a five-point Likert scale [46] ranging between −2 and 2. With the result, we calculate the rating of each factor using the arithmetic mean of the evaluations. That value represents the weight that each factor has in measuring the productivity of the developed system. Figure 2 shows the seventy-five factors grouped according to the classes: People, Product, Organization, and Open Source Software and organized in descending order, according to the rating. The details of each of the seventy-five factors can be consulted at the Table A1–Table A4.

3.2.2. The Gamification Project

We conducted another online survey to identify the characteristics of the software development teams to understand the target audience, which is necessary for the gamification project [32]. Sixty-nine software developers responded to this survey. Participants assessed the questions using a Likert scale [46] (between 1 and 5) how much the octalysis core drives (Section 2.2) influence themselves. In this context, the averages among all the responses ordered in descending order were 1. C5 (4,34); 2. C7 (4,26); 3. C3 (4,19); 4. C1 (4,02); 5. C2 (3,91); 6. C6 (3,78); 7. C4 (3,25); and 8. C8 (3,04).

Based on the influence of core drives on the target audience, we choose the gamification techniques applied in the web system. Chou [32] presented more than ninety gamification techniques, each associated with at least one core drive. Among these, the techniques chosen were 1. Trophy room (associated with the C5 core drive); 2. Visual storytelling (associated with the C7 core drive); and 3. Combos (associated with the C3 core drive). In addition to these three gamification techniques, the system also uses the Leaderboards technique [32] associated with the C2 core drive since this technique can be applied as a motivating factor for productivity when used in software development [40].

3.2.3. Functionalities of the Web System

In general, our measurement system can use all factors found in the literature. However, as some factors may not be relevant in a given context, the developed software allows users to add or remove these factors.

For the assessment of factors to occur as expected, our tool has the functionality of measuring productivity. In addition, to calculate productivity, our system has other resources, such as 1. user registration; 2. company registration; 3. factors registration; 4. user login; 5. trophy room; 6. leaderboards; and 7. Combos. The development and detailing of features is reported in the Section 4. Such planning sought to use the gamification techniques selected and described in Section 3.2.2.

3.3. Construction

As with other complex systems, software systems evolve, which means that functional and non-functional requirements can frequently change [47]. The incremental development model, according to Pressman [47], is an evolution of the waterfall model (traditional) where, instead of specifying and developing all the requirements at once, we work with the construction of small pieces of software separately (in iterations).

In this work, the incremental model was combined with component-based development that, according to Pressman [47], foresees the construction and use of reusable components. The modularization of the system, in turn, will help in the use of another concept: the Single Responsibility Principle (SRP), which states that each module or class must have responsibility for a single part of the functionality provided by the software [48]. The use of the SRP allows, in addition to the organization, benefits such as ease in the identification and correction of errors, since the code is more granular [49]. In addition, we built the entire system following test-oriented modeling. This approach incorporates consistency and semantics checks during system engineering and therefore helps to produce high-quality models [50].

Figure 3 represents the architecture of the open-source system we developed. Such a system can be divided into the three conceptual layers described below.

Frontend Layer: where is the application developed with Angular, a framework created by Google developers to build the application interface using HTML, CSS, and JavaScript (https://angular.io/). The code for this layer is available on GitHub.
Backend Layer: layer which contains the two microservices built in this work. The “User Microservice“ (code available on GitHub)) is responsible for all operations related to user registration, while the “Productivity Microservice“ (also available on GitHub) is responsible for operations related to the calculation of productivity. Microservices architecture is used as an alternative to monolithic applications because they are simpler to scale, are more flexible, and allow for different contexts to be handled in different code units [51]. The language used to build the microservices was Java with the Spring framework that “makes programming in Java faster, easier and safer”, in addition to being the most popular Java framework in the world (https://spring.io/why-spring/).
Data Layer: we used the microservices architecture in conjunction with the Database per Service pattern that helps ensure that services are loosely coupled and that changes to a service’s database do not affect any other service. The DBMS (Database Management System) selected was PostgreSQL, which “is a powerful open source object-relational database system” (https://www.postgresql.org/).

As for the features developed and related iterations, Table 1 contains a relationship where the first column represents the functionality and the second column represents the iteration.

3.4. Case Study

We conducted a case study with two software development teams. The teams had a total of four participants each, totaling eight participants. Among the participants, six were developers and two were project managers. The development methodology used by the two teams was agile, but they did not follow a specific one (such as Scrum [52]). Instead, the teams incorporated elements they judged to be positive, such as the daily meeting of up to 30 min (instead of the 15 provided in Scrum); sprints with their size defined by the backlog desired by the client (varied duration); and a peer review. The sprint tasks were organized in a three-column Kanban board: To Do, Doing, and Done.

One of the projects had its development started in April 2019 (here, called Team 1). Until the application of the case study of this work, they used no form of productivity measurement. The second project (here, called Team 2) started in February 2021.

When the case study started, Team 1’s project already had several deliveries to the customer and was nearing the end of its planned development phase. After that, the sustain phase started. However, for the two projects to be evaluated in more similar scenarios, the productivity measurement only took place until the completion of the development stage. The second project (from Team 2) had the productivity measurement started in the first months of its implementation, during the development stage.

Another characteristic of the two teams is that they both work remotely. Team 1 contained people who worked face-to-face together and started working remotely in March 2020. Team 2 had people who have never met face-to-face and who, since its inception (in February 2021), had worked exclusively remotely. All participants of both teams were Brazilians residing in Brazil and living in different states.

Table 2 presents the demographic information of the development teams. Through Table 2 it is possible to see that Team 1 and Team 2 have some similar characteristics: the average experience of Team 1 is 14 years (considering “More than 30 years" with the value 30 for this calculation); while the average for Team 2 is 12 years (considering “Over 20 years” with the value 20 for this calculation); and all participants are male.

As for the people’s roles on the project, both have a manager and three people from technical roles. However, Team 1 has an integration architect (not present in Team 2), and Team 1 has no distinction between front-end and back-end developers—both developers are full-stack. Meanwhile, Team 2 has a front-end manager, a back-end manager, and a full-stack developer who works on the front-end and back-end.

Table 2 also shows the schooling degree of all participants. Team 1 consists of two graduates, a postgraduate, and a master’s student, that is, all of them have at least a college degree. The second team consists of a postgraduate, a graduate, a master’s student, and a person with only a high school education.

4. Results

Canedo and Santos [22] described a list of thirty-seven factors that influence productivity divided into four groups: People, Product, Organization, and Open Source Software projects. In our paper, we used the same grouping structure and all thirty-seven factors. However, we found in the literature and added to the list other thirty-eight factors. All seventy-five factors are detailed in the Table A1–Table A4.

People: This group contains nineteen factors related to the characteristics of the people who participate in the software development team. The factors in this group include aspects related to the individual.
Product: It encompasses the fifteen factors related to the characteristics of the software product itself. The factors present in this include business field, application complexity, and programming language.
Organization: The twenty-eight factors related to the organization include work environment, knowledge management, team size, and maturity.
Open Source Software Projects: This group of thirteen factors represents those related to free software projects. The factors of this group include investments in Information and Communication Technology (ICT), contractual relations, and team engagement.

4.1. The Application

The application has a series of features that allow measuring the productivity of a team. The first is the login functionality, through which the user must enter their e-mail and password to access the application. After login, the user sees (Figure 4) a menu configured according to their permissions on the home screen. If it is a regular user, the only menus displayed will be the Dashboard, Measurements, User Profile, and Trophy Room. If it is an administrator, the system shows all of these and three others: Companies, Users, and Factors.

In addition to the menu, the screen also shows a Billboard. The role of this table is to provide information to the user in a clear and easily accessible way (as it is the first information after login) and in a way that can help motivate the user. Right after the Billboard, the screen shows the user a Loyalty Leaderboard. This feature orders the users who have contributed most to measurements since the system started. We highlight that leaderboards do not directly impact the productivity measurement and correspond to one of the gamification techniques used in this work.

The system displays the Productivity Leaderboard in the last column on the right. There is a process that generates the leaderboards for the current month. The responsible for the project can choose to execute it on the date they want. To calculate the productivity score, the system considers all productivity measurements for a development team during that period.

The company’s score consists of the arithmetic mean of all its evaluations. Besides that, there is a possible increase of 10% if the company has performed a Combo, that is if it has made measurements in all the weeks of the month. The use of Combos refers to one of the gamification techniques chosen for this project. After calculating the score, the leaderboard is assembled by sorting the respective scores in a decreasing manner, that is, from highest to lowest. It is noteworthy that, despite the process being carried out for all teams registered in the database of the Productive tool, only the first three teams with the highest score are displayed on the dashboard.

4.1.1. The Admin Features

The set of administrative functionalities of the Productive tool consists of Companies, Users, and Factors. Through the Companies menu, as shown in Figure 5, the administrator user can view a list of all companies registered in the system. The user also has the possibility to add a new company. To add a new company, the user has to assign the company a name not yet used in the system. It is worth mentioning that, when adding a company, the system automatically allocates the user responsible for the addition to that company. After that, other existing users can be allocated as members of the company team.

Through the Users Menu, the administrator user is able to view a list of all users registered in the system, and he/she also has the possibility to create a new item. To create a user, as shown in Figure 6, it is necessary that the user who is making the registration provide their e-mail, name, date of birth, gender, and Github profile. In addition, the system automatically sets a random password for accessing the application. It is worth mentioning that, at the time of its creation, a user still does not have an allocation to any company.

The Factors menu shows a list of all factors that influence productivity (seventy-five factors identified in the literature registered in the application. This functionality also allows the user to register new factors. Figure 7 shows the factor registration screen, where we can see that, to register a new, the user must provide a name, description, weight, the type of user responsible for registering it, and a category. Initially, the factors’ weights are the values found and presented in Section 3. In addition to the registration, a factor can also be activated or deactivated at any time in the application. When a factor is in "Inactive" status, the system will not use it when calculating new measurements.

4.1.2. Common Features

As soon as all the settings are ready, that is, all of the registered factors, a company created, users created, and allocated to a company, a common user will be able to access the system. The set of common features available consists of the Measurements, User Profile, and Trophy Room. The User Profile functionality is responsible for showing the logged-in user information related to their registration, namely name, e-mail, birth date, gender, and profile on Github.

In addition to this information, as shown in Figure 8, a list of all user allocations is also displayed. The user’s allocation represents the relationship between a user and a company. This list shows the following information: allocation ID, company name, role, start date, and end date. At the end of the allocation list, if the user has an administrator profile, the Add Allocation button is also displayed, allowing the user to add allocations to new companies.

The Trophy Room functionality is where the user can check the trophies won by the teams in which he/she is allocated. When clicking on the related menu, the system redirects the user to the screen shown in Figure 9.

Figure 9 shows a slider with three pages: first, the system displays the gold trophies won by the teams of the logged-in user; the second shows the silver trophies; and the third shows the bronze trophies. If the user does not have any trophies at any of the levels (gold, silver, or bronze), the related page will display a message stating: “What a pity, you and your team have not won any trophies yet”.

When clicking on the Measurements menu, the system directs the user to the initial screen of the Measurements functionality, which as was adopted by default for other functionalities, contains a list of items. The system displays only measurements related to companies for a user in which they have an allocation.

To measure productivity, a user must fill in the form presented in Figure 10. All users registered in the tool and with an active allocation to a company can input the data. The system displays the name of the user, the name of the company, and the date of the evaluation. At the bottom of the screen (section Measuring Factors), the system displays a form where each field represents a factor registered in the factor registration functionality.

As for the form’s assembly, the system displays only factors that are registered and active. Furthermore, a user only evaluates a factor if the factor is set to be filled by users of his role. In addition, we highlight that users evaluate all factors in a scale with numbers between 1 and 10. For each factor, the text displayed above the input box corresponds to the factor’s name, while the text displayed below this box corresponds to the description registered to the factor.

Considering that a team’s measurements are visible to all its participants, to monitor if the tool is being used regularly and properly, we encourage users to make team productivity measurements a frequent topic at meetings, discussing outliers whenever they arise.

Listing 1

How we calculate productivity.

private List<CompanyAssessment> calculateProductivity(List<UserAssessment> lastMonthItems) {

// an item of this list represents a company and its productivity

List<CompanyAssessment> assessmentsByCompany = new ArrayList<>();

// Scroll through the list of all measurements created in the last month

// this list contains an item for each user assessment.

lastMonthItems.forEach(userAssessment -> {

if (assessmentsByCompany.stream().anyMatch(abc -> abc.getCompanyId()

.equals(userAssessment.getCompanyId()))) {

// if the valuation is for a company that already exists: update item

CompanyAssessment valComp = assessmentsByCompany.stream()

.filter(abc -> abc.getCompanyId().equals(userAssessment.getCompanyId()))

.collect(Collectors.toList()).get(0);

// the productivity of the company is given by the allocated users assessments

// so here we take as result the mean between the current value

// and the value of the user assessment

double productivity = (valComp.getProductivity() + userAssessment.getAssessment())/2;

// if its a combo, company gets 10% extra

// a combo happens when the company has measurements in all the weeks of the month

productivity = isCombo(lastMonthItems, userAssessment.getCompanyId())

? (productivity * 1.1) : productivity;

valComp.setProductivity(productivity);

} else {

// if the valuation is for a company that does not yet exist: add item

// in this case, the productivity value is simply the users’ evaluation

CompanyAssessment newItem = CompanyAssessment

.leaderboardId(0L)

.empresaId(userAssessment.getCompanyId())

.productivity(userAssessment.getAssessment())

.build();

assessmentsByCompany.add(newItem);

}

});

return assessmentsByCompany;

}

We stated earlier that we calculate a company’s productivity through the arithmetic average of its users’ ratings. Listing 1 shows the code we used to perform the productivity calculation. We review all the code contained in Listing 1 with explanations of what each part of it does.

4.2. Case Study Results

As described in Section 3.4, the object of this case study was composed of two software development teams. To start the productivity measurement process, we registered both companies in the application as well as the users who participated in the measurements and created allocations between people and the respective companies. The developed system allows for the factors that are used by a given software development team to be changed through the system itself. This functionality is important because some factors may not apply to a given context since the context is one of the modifiers of the perception of productivity [5].

Thus, to allow for this research to compare the results of the two development teams according to similar parameters, the members of the two teams talked with each other to decide which factors would be used to perform the productivity measurements in the case study. The members chose twenty-one factors out of seventy-five possible with the justification that these are the most related to their development teams. Table 3 contains the twenty-one factors selected by the two teams.

Before measurement started, users needed to know how to use the system. For this, we recorded and distributed a video containing a simulation of the use of the tool. Although there were plans for the later addition of gamification-related features, we did not pass this information to users. After we added gamification, we gave no new explanation, so users learned to use these features on their own.

For organizational purposes, the two teams agreed that the measurements would start on Fridays and end on Saturdays. Thus, people who did not record their measurements in this period did not have their perception of the team’s productivity computed.

The x-axis of the graphs shown in Figure 11 and Figure 12 represents the week in which the measurement happened. The y-axis represents the arithmetic mean of the measurements of all users allocated to the team during the week. It is worth remembering that each allocated user (team member) records one measurement per week.

Figure 11 shows Team 1’s progress over the eight weeks that we applied the productivity measurement process. It is possible to observe in Figure 11 that the arithmetic mean of the evaluations increased slowly over the weeks—with 7.81 being the arithmetic mean of the first week and 9.11 the value corresponding to the last week, showing an improvement of 1.3 points in the period. The general mean, that is, of all values for all weeks, was 8.35; the median was 8.11; and the standard deviation (considering the arithmetic mean of each week) was only 0.539. Considering these values, we can see that the dispersion is small, meaning that the results do not tend to deviate much from the average between one week and another.

Figure 12 shows Team 2’s progress over the eight weeks that we applied the productivity measurement process. According to the information presented in Figure 12, it is possible to observe an atypical behavior in the first three weeks, followed by a gradual improvement in the last five weeks. According to the team’s report, the drop in productivity occurred in the third week due to internal team disagreements caused by a specific situation during the development process that they already resolved. This situation caused a decrease in some factors evaluation that made the overall average decrease.

The arithmetic mean of the first week was 7.16, with 8.94 being the value corresponding to the last week, showing an improvement of 1.78 points in the period. The overall mean was 7.90. The median value was 7.97, and the standard deviation, considering the arithmetic mean of each of the eight weeks, was 0.816. This value was more significant than the standard deviation of 0.539 found for Team 1, representing 151.39% of this value. Considering these values, we can measure a relatively large dispersion, meaning that for Team 2, results far from the mean are more common.

4.2.1. RQ.1. What Is the Effect on Productivity Assessments of Adding Gamification?

As mentioned, the gamification-related features were only available from the fourth week onwards. In other words, just in these weeks, participants had access to the Leaderboard to see the position of their team, had visual stimuli to perform the tasks (such as those on the Billboard), had access to the Trophy Room, and possibly won trophies and combos had effects.

As shown in Figure 11, if we consider the average of Team 1’s weekly ratings before adding gamification, we have a value of 7.92. The average after adding the gamification elements was 8.78—a difference of 0.86 points. The standard deviation before the addition of the gamification elements was only 0.096. After that, the value raised to 0.418—that is, results far from the mean became more common.

Subtracting the value of the arithmetic mean of the evaluations obtained in one week by the same value obtained in the previous week, we have the value that represents the evolution from one week to another. The average evolution in the weeks before the addition of gamification for this team was 0.077 points per week, while the same average in the weeks after the application of gamification was 0.268 points, representing a more significant value (higher average weekly evolution).

Regarding the impact of inserting the gamification elements on the results obtained for Team 2, if we consider the average of the weekly evaluations of Team 2 (Figure 12) before this, we have a value of 7.25. The average after adding the gamification elements rose to 8.55—a difference of 1.3 points. The average evolution in the weeks before applying gamification for Team 2 was 0.243 points per week. The same average in the weeks after gamification was 0.263 points—a higher value represents a better evolution per week. However, this result is lower than that obtained by Team 1, which had an improvement of 179.16% in the average weekly evolution (going from 0.077 before the addition of gamification and reaching 0.268 after the addition), while the improvement obtained by Team 2 was 8.23% (out of 0.243 and reaching 0.263 points). The standard deviation before the addition of the gamification elements was only 0.549. After that, the value raised to 0.364—that is, results far from the mean became less common.

Before adding the gamification elements, the two teams already had a small improvement in the weekly productivity rating. For this reason, we used the metrics general average of the evaluations and average weekly evolution. We used both metrics for comparing the results obtained before and after the addition of the gamified elements.

When we analyze these metrics, the two software development teams had bigger improvements in productivity after adding gamification; however, they manifested differently: Team 1 had a steady and more linear improvement, while Team 2 had a more visible variation in the results in the first three weeks and started to evolve more linearly from the fourth week onwards.

In addition, we used one more auxiliary metric to assess the impact of gamification: the standard deviation. For Team 1, this metric indicates that, after the addition of gamification, the results show a higher standard deviation, that is, values that are farther from the mean and, consequently, less linear. The numbers obtained for Team 2 indicate, however, that the results became more linear after the addition of gamification. However, it is valid to remember the incident that occurred with this team in the third week, before the gamification, which changed the linearity of the results over the period.

4.2.2. RQ.2. What Is the Perception of Users concerning the Productivity Calculation System?

Despite the positive results presented in Section 4.2.1, we understand that we need validation of the perception of the users regarding the productivity calculation system. For this reason, at the end of the eight weeks of measurement, we applied a survey so that system users could assess their perceptions over the system. This survey consisted of only three questions:

Q1: How much do you agree that using the productivity measurement tool helped the team improve productivity?
Q2: How much do you agree that using gamification helped improve your team’s results?
Q3: Considering your team’s productivity graph throughout the measurement process, how much do you agree that it represents reality?

The respondents answered all the questions in the survey using a Likert [46] scale with five options, the first representing total disagreement and the last representing complete agreement. For the mathematical analysis, the options were weighted with the corresponding number between 1 and 5. The option that represents total disagreement has a value of “1“, and the one that represents complete agreement has a value of “5“.

Figure 13 represents the evaluations made by the members of Team 1. The average of the answers for Q1 was 4.25; for Q2, it was 4.75; and for Q3, it was 4.5. Thus, all users of Team 2 considered 1. that the use of the productivity measurement tool helps the team to improve its productivity; 2. that the use of gamification helped to improve these results; and 3. that the measurements performed in the system correspond to reality—since the graph we have shown to the team was the graph referring to its progress (Figure 11).

Figure 14 represents the evaluations made by the members of Team 2. For this team, the average of the answers for Q1 was 4.5; for Q2, it was 4.5; and for Q3, it was 5. That is, all users of Team 2 considered 1. that the use of the productivity measurement tool helps the team to improve its productivity; 2. that the use of gamification helped to improve these results; and 3. that the measurements performed in the system correspond to reality—since the graph we have shown to the team was the graph referring to its progress (Figure 12).

When we compare the results for Team 1 (Figure 13) and Team 2 (Figure 14), we can see similar behavior since both evaluations were positive. In the first question, the evaluation of Team 2 was superior by 0.25 points. The members of Team 2 consider that the use of the productivity measurement tool helps the team to improve productivity more. For the second question (Q2), we note that the evaluation of Team 1 was higher by 0.25 points (the members of this team consider the use of gamification more effective). We highlight that the users’ perception was consistent with the productivity measurement result since, as shown above, gamification had better results with Team 1. Concerning Q3, Team 2’s ratings averaged 5 (that is, all members had the maximum degree of agreement) and showed a result with a 0.5 point advantage over Team 1.

Considering the evaluations of all participants, the average of the answers for Q1 was 4.375; for Q2, it was 4.625; and for Q3, it was 4.75. Thus, we consider that (according to the results obtained in the measurement process and to user feedback) the use of the measurement tool helped teams to improve productivity, using gamification improved results, and the value corresponding to productivity contained in the platform represents the users’ perception of reality well.

5. Threats to Validity

This research presents some threats to validity. The first threat is the number of responses we obtained for the surveys. The survey to identify the characteristics of the target audience obtained sixty-nine answers. The survey to assess the productivity factors obtained forty-four answers. The number of survey participants can be considered a threat to validity as this number does not represent a large scale of the members of the software development teams, so the knowledge extracted from the survey responses may not be valid for all development teams. To mitigate this threat, we aimed to find teams with different characteristics that operate in the software industry in multiple contexts.

As for the measuring productivity way, in our work, the productivity of a team is given by its members; therefore, there is a possibility of a loss of accuracy due to biased evaluations.

Another threat is related to the sample size that participated in the case study. We carried out the case study with two teams of four people each. Thus, we understand that the sample size is a threat to validity, as we may identify different behaviors and results in larger samples. In future experiments, we will perform a study with a more diverse sample and with more participants. Finally, we also considered the duration of the case study to be a threat since the participants used the tool for only eight weeks. In the future, we will mitigate this threat by conducting experiments during all phases of the software development process and with a larger sample of participants and projects.

6. Conclusions

This paper presented a tool built to measure productivity according to factors found in the literature. In the measurement, each factor can have a different weight, and to obtain these weights, software development teams’ members evaluated them in a survey. To motivate users to use the tool, we developed it using gamification techniques. To select these, members of software development teams assessed which gamified elements have the most effect on them through a survey.

When we compare the results of the two software development teams (Team 1 and Team 2), it is possible to see that, in general, both showed an improvement in productivity after starting the measurements. However, this improvement manifested itself differently: while Team 1 had a steady and more linear improvement, Team 2 had a more visible variation in results in the first three weeks and started to evolve more linearly from the fourth week onwards. We highlight that Team 2 started the project a short time ago and, its members did not know each other that well yet. Thus, certain situations can affect the perception of Team 2 members about their productivity due to a lack of knowledge and trust in other team members.

We also observed that the productivity increased after the addition of the elements related to gamification, demonstrating a positive effect of these. We applied a survey to obtain users’ feedback about the tool’s effectiveness and had positive results: users agree with the tool’s usefulness and agree with the numerical results shown.

As future work, we will assess the validation of the tool in other contexts with a larger number of participants in the development teams to investigate the impact of using the tool in measuring the productivity of teams in larger software projects.

To enable the use of our tool in the context of software development teams that already use other tools where members must input data, another future work is building integrations with external systems. Finally, to speed up the form completion task, we propose the development of an auto-completion feature of the productivity evaluation form data based on the last user evaluation as a future work.

Author Contributions

Writing—original draft preparation, J.S.M., E.D.C., and H.A.T.; writing—review and editing, J.S.M., E.D.C., and H.A.T.; visualization, J.S.M., E.D.C., and H.A.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures and Tables

Figure 1. Steps and procedures adopted in the development.

Figure 2. Factors that influence Productivity and their respective rating.

Figure 3. Platform architecture.

Figure 4. Dashboard screen.

Figure 5. Companies list.

Figure 6. User registration.

Figure 7. Factor registration.

Figure 8. User profile.

Figure 9. Trophies Room.

Figure 10. Productivity measurement.

Figure 11. Team 1 progress.

Figure 12. Team 2 progress.

Figure 13. Team 1 user evaluation about the tool.

Figure 14. Team 2 user evaluation about the tool.

Table 1

Features and iterations.

Feature	Iteration
User registration	1
Login	1
Companies registration	2
Factor registration	2
Measurement with registered factors	3
Leaderboards	4
Trophy room	4
Combos	4

Table 2

Profile of the development teams.

ID	Team	Role	Experience	Level
P01	1	Project Manager	more 30 years	Graduate
P02	1	Full-Stack Developer	7 years	Postgraduate
P03	1	Integration Architect	14 years	Graduate
P04	1	Full-Stack Developer	5 years	Master’s student
P05	2	Back-End Developer	8 years	Postgraduate
P06	2	Front-End Developer	15 years	High school
P07	2	Project Manager	more 20 years	Graduate
P08	2	Full-Stack Developer	5 years	Master’s student

Table 3

Selected factors for the case study.

Category	Selected Factors
People	1. Collaboration between team members
	2. Ease of communication
	3. Motivation
	4. Team cohesion
	5. Skills and competences
Product	6. Lost time
	7. Poor code quality
	8. Completeness of design
	9. Requirements
	10. Quality
	11. Adequate documentation
Organization	12. Code reuse
	13. Feedback Culture
	14. Access to information
	15. Efficient meetings
	16. Working environment
	17. Trust in other members
OSS	18. Organizational diversity
	19. Application complexity
	20. Team engagement
	21. IT investments

Appendix A. Factor Detailing

Table A1

People factors.

Category	Factor	Detailing	Rating
People	Focus	the focus factor measures how focused the team is on achieving the iteration goal [53].	1.537
People	Collaboration between team members	collaborative work positively influences productivity in software development [22].	1.45
People	Ease of communication	in collaborative development environments, the ease of communication between members is an important factor in increasing productivity [22].	1.45
People	Motivation	the motivation of those involved in software design positively influences productivity in software development [22].	1.425
People	Commitment	it represents the responsibility level that team members are willing to assume in their tasks within their work team [54].	1.415
People	Team cohesion	cohesion is one of the factors that positively affect productivity [22].	1.325
People	Mental health	the good mental health of employees leads to productivity gains [4].	1.3
People	Skills and competences	even without identifying the types of skills and competencies, the relationship between them and productivity is positive [22].	1.275
People	Satisfaction	this dimension captures human productivity factors and has several possible subcomponents, including physiological factors, such as fatigue and team comfort measures. [5].	1.1
People	Enthusiasm	the degree of enthusiasm for the work is the factor that most affects the productivity of developers [55].	1.1
People	Experience	the team members’ experience positively influences productivity in software development [22].	1.025
People	Physical health	the good physical health of employees leads to productivity gains [4].	0.975
People	Technical qualification	technically well-qualified developers can be more productive because that allows them flexibility in the tasks they can perform [56].	0.975
People	Happiness	developers’ happiness positively impacts team productivity [25].	0.95
People	Quality of life	the literature points out that the quality of life at work is related to the organization’s productivity [57].	0.925
People	Behavioral qualification	the good behavioral qualification of the developers involves focus, concentration, tranquility, commitment [56].	0.775
People	Availability of members for allocation	availability of members for allocation to the development team: having the resources available in the necessary time is an important aspect and positively influences productivity [22].	0.7
People	Home distractions	in home-office it is common for people to lose productivity because they are distracted from their activities [58].	0.488
People	Turnover	the lower the resource turnover within a project, the better for productivity. In such a way, this is a factor of negative influence [22].	0.175

Table A2

Product factors.

Category	Factor	Detailing	Rating
Product	Quality	represents the quality of the work conducted. Such a metric can be obtained according to internal values (for example, the quality of the code and the number of bugs produced) or external (for example, the quality of the product from the perspective of the end users) [5].	1.3
Product	Adequate documentation	this factor represents how well the documentation fits the needs [59].	0.95
Product	Requirements	considering the dependence on several factors (such as ambiguity and volatility of requirements) the influence of this factor varies according to the context [22].	0.85
Product	Business area	it consists of the business area covered by the software, and its impact varies according to the product area [22].	0.725
Product	Completeness of design	it shows that the more complete the design when starting development, the better [59].	0.725
Product	Poor code quality	the lack of quality in the code developed directly impacts the motivation and productivity of developers [25].	0.7
Product	Technological platform	each platform has a productivity impact, so an organization’s analysis of historical bases is needed to define which is more productive [22].	0.675
Product	Programming language	the higher the level of abstraction of the language used in the solution, the better software development productivity [22].	0.625
Product	Project duration	the duration of a project is a factor that negatively affects productivity [22].	0.6
Product	Application complexity	can be defined as the degree of difficulty for a project or part of it [22].	0.575
Product	Speed	is the ratio of the time spent required to perform a given amount of work. This factor, as presented by [5], resembles Sharpe’s definition of productivity [7].	0.575
Product	Lost time	a quarter of developers’ working time is wasted, and additional code analysis and technical debt cause it [60].	0.5
Product	Number/frequency of commits	Helie et al. [41] classify the frequency of code commits in an hour interval as a factor to define productivity. According to the authors, a high number means (according to empirical knowledge) that more work is conducted.	0.4
Product	Software size	the reason for the negative relationship between productivity and software size is the increased complexity of the project [22].	0.35
Product	Type of software developed	the different types of systems have different influences on productivity [22].	0.25

Table A3

Organization factors.

Category	Factor	Detailing	Rating
Organization	Trust in other members	the ability of team members to trust each other influences productivity [61].	1.61
Organization	Work environment	the work environment contains aspects that together positively influence productivity [22].	1.415
Organization	Efficient meetings	the efficiency of meetings and their related practices also affects the productivity of development teams [55].	1.415
Organization	Access to information	productivity is positively impacted in a software creation environment where the flow of information between humans and the tools involved is optimized [35].	1.29
Organization	Feedback Culture	performance feedbacks influence how well developers produce [55].	1.22
Organization	Code reuse	the reuse of code, libraries, or even functionality is a factor that positively impacts productivity in software development [22].	1.195
Organization	Maturity	is one of the factors that most positively affect productivity and requires a team with effective communication, high adaptability, conflict management skills, shared decision-making, cohesion, mutual trust, behavioral compliance, clear responsibilities, and shared responsibilities [19].	1.195
Organization	Use of best practices in software project management	practices that support the construction of a work environment that favors the commitment and interest of team members are factors of positive impact on productivity [22].	1.15
Organization	Merits and rewards system	these systems contribute positively to the productivity of the development team [22].	1.098
Organization	Accuracy of information	the accuracy of the information that reaches the development team (such as bug reports, use cases, and change requests) influences its productivity [27]	1.098
Organization	Team autonomy level	it subjectively represents the extent to which the software team has authority and control in making decisions to carry out the project [62].	1.073
Organization	Stakeholder participation in development	in general, it affects productivity positively, but if excessive, this participation can be negative [22].	1.07
Organization	Knowledge management	the lack of knowledge exchange between developers is a factor that negatively influences productivity [22].	1.05
Organization	Work Tools	the use of good work tools also influences productivity [55].	1.05
Organization	Trainings provided by the company	the existence of training is a factor that improves productivity by allowing the acquisition of significant knowledge for software development [22].	0.98
Organization	Use of auxiliary tools	no matter how much using different tools requires effort, its use is considered a factor that positively impacts productivity [22].	0.951
Organization	Software processes	the improvement of processes leads to improvements of other aspects, such as reuse, the flexibility of adaptation, and process stability achieved under conditions of high maturity. In such a way, this is a positive factor [22].	0.951
Organization	Development site	studies indicate that the development location affects productivity (for example, different countries, military or industrial organizations, etc.) [22].	0.93
Organization	Sharing members between projects	resource sharing between projects is negative for productivity as developers have to keep different contexts in mind [22].	0.829
Organization	Innovative mindset	the existence of a mindset that is always open to new ideas influences the productivity of software developers [55].	0.829
Organization	Iteration length	the length of an iteration in days, calculated as the time elapsed between the start and end dates of the iteration, can affect productivity [53].	0.756
Organization	Existence of Rework	the existence of rework is negative for productivity as it indicates some other negative aspects as the existence of defects [22].	0.63
Organization	Variety of tasks	the variety of types of tasks is one of the factors that affect the productivity of software developers [55].	0.54
Organization	Team size	small teams made up of experienced developers have better levels of productivity [22].	0.51
Organization	Possibility of remote work	the possibility of doing the work remotely to perform tasks that require uninterrupted concentration positively affects productivity [55].	0.49
Organization	Existence of historical measurement history	the existence of historical data positively influences productivity, as such data can serve as a support for comparison and because they can also allow for a better understanding of the behavior of software projects [22].	0.46
Organization	Homogeneity	teams with the highest homogeneity levels are more productive, produce better quality code, and are more effective in testing [63].	0.415
Organization	Software risk exposure level	represents the level of project uncertainty, having a noticeable impact on how the software can respond to business needs over time [62].	0.075

Table A4

OSS factors.

Category	Factor	Detailing	Rating
OSS	Investments in Information and Communication Technology	comprises investments in software, hardware, and laboratories and is a factor of positive influence on productivity [22].	2
OSS	Team engagement	in general, developers of open-source software projects are more motivated to contribute and, in addition, there is a very positive exchange of experiences between these individuals [22].	1.25
OSS	Developer base	it is natural for developers to be more interested in contributing to open-source software projects that have more developers contributing [64]. Ref. [65] stated that in larger development communities (with a large number of participants) developers are more active.	0.89
OSS	Application complexity	a modularized architecture without complexity makes it easier for other people to contribute to the project, thus making it more productive [64].	0.82
OSS	User base	it is natural for developers to be more interested in contributing to open-source software projects with a larger user base [64].	0.64
OSS	Contractual relations	establish more security for developers and therefore make development more productive [22].	0.54
OSS	Entry barriers	barriers to entry can directly impact productivity in open-source software projects [22].	0.45
OSS	Organizational diversity	open-source software projects with the best organizational diversity, where people from different companies contribute, have better productivity [65].	0.43
OSS	Team disengagement	open-source software developers may lose interest in the project due to several factors faced by these [22].	0.42
OSS	Gender diversity	teams composed of men and women bring different perspectives and, thus, have better results [22].	0.32
OSS	Size correlation (commits X contributors)	Jiang et al. [65] considered, in open-source software development projects, the correlation between the number of commits and the number of contributors as the main factor for productivity.	0.32
OSS	Project age	software productivity gradually decreases after reaching a peak in the project development cycle. That indicates that project age affects productivity [66].	0.275
OSS	Lack of contractual relationships	the lack of contractual relationships allows contributors to free software projects to spend their time contributing to activities that directly increase software productivity [22].	0.09

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Despite efforts to define productivity, there is no consensus in the software industry regarding what the term productivity means and, instead of having only one metric or factor that describes productivity, it is defined by a set of aspects. Our objective is to develop a tool that supports the productivity measurement of software development teams according to the factors found in the literature. We divided these factors into four groups: People, Product, Organization, and Open Source Software Projects. We developed a web system containing the factors that influence productivity identified in this work, called Productive, to support software development teams in measuring their productivity. After developed the tool, we monitored its use over eight weeks with two small software development teams. From the results, we found that software development companies can use the system to support monitoring team productivity. The results also point to an improvement in productivity while using the system, and a survey applied to users demonstrates the users’ positive perception regarding the results obtained. In future work, we will monitor the use of the tool and investigate the users’ perceptions in other project contexts.

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Title

Tool for Measuring Productivity in Software Development Teams

Author

Jhemeson Silva Mota¹

; Tives, Heloise Acco²

; Edna Dias Canedo¹

¹ Department of Computer Science, University of Brasília (UnB), P.O. Box 4466, Brasília 70910-900, Brazil; [email protected]
² Computer Science Department, Federal Institute of Paraná (IFPR), Curitiba 80000-000, Brazil; [email protected]

First page

396

Publication year

2021

Publication date

2021

Publisher

MDPI AG

e-ISSN

20782489

Source type

Scholarly Journal

Language of publication

English

DOI

https://doi.org/10.3390/info12100396

ProQuest document ID

2584397444

Tool for Measuring Productivity in Software Development Teams

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