Understanding and Evaluating Developer Behaviour in Programming Tasks

Centrala begrepp

Developers' behavior in programming tasks can be evaluated and understood through recorded interactions with the IDE, revealing distinct phases and action patterns that impact comprehension and task-solving performance.

Sammanfattning

The article discusses how developers' behavior is recorded and interpreted in programming tasks to evaluate their performance. It highlights the use of the MIMESIS plug-in to capture interactions with the IDE, focusing on source code files visited, code edits performed, and knowledge expansion strategies. The study design involved scenarios for participants to complete specific programming tasks, revealing a variety of approaches used by developers. Results showed different working patterns among participants, indicating distinct phases like investigation, editing, and validation. A behavior coding system was developed to assess comprehension and task-solving metrics based on observed actions and patterns.

Statistik

N=51 recordings were selected for further investigation.
Only a part of participants (𝑛𝑑 = 14) used debugging functionality.
Time spent on solving tasks ranged from under 10 minutes to almost 2 hours.
Cyclissity metric was introduced to express navigation behavior.

Citat

"In contrast to our work, they focused on actions of end-users debugging Microsoft Excel spreadsheets based on think-aloud protocols."
"The behaviour of developers reading the source code file, performing some edits, then switching back to the instruction file before returning to the code file may be compared to the 'Restart' pattern."
"Developers seem to recursively switch forth and back between different stages of comprehension."

Viktiga insikter från

Understanding and Evaluating Developer Behaviour in Programming Tasks

by Mart... på arxiv.org 03-14-2024

https://arxiv.org/pdf/2403.08480.pdf

Djupare frågor

How can the findings from this study be applied practically in software development processes?

The findings from this study provide valuable insights into how developers interact with Integrated Development Environments (IDEs) while working on programming tasks. By understanding the specific behaviors and action patterns exhibited by developers, software development teams can tailor their training programs to enhance comprehension and task-solving skills. For instance, identifying common navigation strategies or debugging techniques that lead to successful task completion can inform the creation of best practices for developers. Additionally, utilizing metrics like cyclissity to evaluate developer performance can help in assessing individual strengths and areas for improvement.

What are potential limitations or biases in evaluating developer behavior solely based on recorded interactions?

One limitation is that recorded interactions may not capture the full context of a developer's decision-making process. Certain nuances such as external factors influencing behavior, internal thought processes, or collaboration dynamics with team members may not be evident from interaction data alone. Moreover, there could be biases introduced by the recording tool itself, leading to incomplete or skewed representations of developer behavior. Another consideration is the lack of emotional cues or non-verbal communication that could impact how actions are interpreted; these aspects are crucial in understanding human behavior comprehensively.

How might understanding developer behavior in programming tasks contribute to improving overall software maintenance practices?

Understanding developer behavior in programming tasks plays a vital role in enhancing software maintenance practices. By analyzing how developers approach program comprehension and problem-solving activities, organizations can identify bottlenecks or inefficiencies in their workflows. This insight enables targeted interventions such as providing additional training resources for specific skill gaps identified through behavioral analysis. Furthermore, recognizing effective strategies employed by high-performing developers allows for knowledge sharing within teams and promotes best practices across projects. Ultimately, a deep understanding of developer behavior leads to more efficient software maintenance processes and higher-quality codebases.