Content area

Abstract

Software systems are becoming popular. They are used with different platforms for different applications. Software systems are developed with support from programming languages, which help developers work conveniently. Programming languages can have different paradigms with different form, syntactic structures, keywords, and representation ways. In many cases, however, programming languages are similar in different important aspects: 1. They are used to support description of specific tasks, 2. Source codes are written in languages and includes a limit set of distinctive tokens, many tokens are repeated like keywords, function calls, and 3. They follow specific syntactic rules to make machine understanding. Those points also respect the similarity between programming language and natural language.

Due to its critical role in many applications, natural language processing (NLP) has been studied much and given many promising results like automatic cross-language translation, speech-to-text, information searching, etc. It is interesting to observe if there are similar characteristics between natural language and programming language and whether techniques in NLP can be reused for programming language processing? Recent works in software engineering (SE) shows that their similarities between NLP and programming language processing and techniques in NLP can be reused for PLP.

This dissertation introduces my works with contributions in study of characteristics of programming languages, the models which employed them and the main applications that show the usefulness of the proposed models. Study in both three aspects has drawn interests from software engineering community and received awards due to their innovation and applicability.

I hope that this dissertation will bring a systematic view of how advantage techniques in natural language processing and machine learning can be re-used and give huge benefit for programming language processing, and how those techniques are adapted with characteristics of programming language and software systems.

Details

1010268
Title
Exploring regularities in software with statistical models and their applications
Number of pages
299
Degree date
2016
School code
0097
Source
DAI-B 77/12(E), Dissertation Abstracts International
ISBN
978-1-339-84498-5
Committee member
Chang, Morris; Govindarasu, Manimaran; Jin, Tian; Zhang, Zhao
University/institution
Iowa State University
Department
Electrical and Computer Engineering
University location
United States -- Iowa
Degree
Ph.D.
Source type
Dissertation or Thesis
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
10126458
ProQuest document ID
1808502334
Document URL
https://www.proquest.com/dissertations-theses/exploring-regularities-software-with-statistical/docview/1808502334/se-2?accountid=208611
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Database
ProQuest One Academic