Investigating the Progression of the Mental Models Formed by Programmers Learning Parallel Programming

Abstract

Research on mental model representations developed by programmers during parallel program comprehension is important for informing and advancing teaching methods including model based learning and visualizations. The goals of the research presented here were to determine: how the mental models of programmers change and develop as they learn parallel programming, the quality of their mental models after learning parallel programming, and what type of information is part of their mental models when examining code for the presence of data races. Participants were experienced C programmers and included both university students and professionals. The mental models of participants were analyzed by having them perform a code tracing task where they externalized their mental models by drawing diagrams while tracing the execution of parallel code. We also analyzed their mental models by having participants determine the presence of data races in parallel code and then answer multiple choice and open-ended questions related to the code. The results presented in this paper indicate that programmers’ mental models progress from a weaker execution model and a stronger situation model before learning parallel programming, to a stronger execution model and a weaker situation model after learning parallel programming. The thematic analysis of the open-ended responses that indicate what components of code programmers used to determine whether or not a data race was present provides insight into the topics that should be emphasized when teaching parallel programming.