Work

Newcomers’ experiences during debugging: A cognitive inclusivity perspective using GenderMag

Year: 2025

Type: article

Abstract: Context: Debugging is a critical practice in software engineering that enables software engineers to ensure the correctness of code by identifying and resolving bugs. It also benefits newcomers as it helps them go through the codebase, understand its structure, and learn about its functionality. Recent research has uncovered that some software engineering tools are not well suited to all ways of thinking, imposing an additional cognitive overhead on individuals whose cognitive styles are not well-supported by the tool. While biases have been explored in other software tools, little is known about whether debugging tools exhibit cognitive biases and introduce “inclusivity bugs”. Objective: This paper addresses this gap by examining inclusivity bugs that newcomers encounter when using the PyCharm debugger. Methods: In this study, we performed a controlled lab experiment where we observed 24 software engineering students with little to no experience as they used the PyCharm debugger for a set of tasks. We used a think-aloud protocol to capture participants’ thoughts throughout the experiment. Then, we conducted a thematic analysis, guided by our research question, to identify potential biases in the tool. We used the GenderMag framework to examine the relationship between cognitive style and the inclusivity bugs. Results: We detail our findings on 21 inclusivity bugs which are caused by 2 main reasons: discoverability and learnability. We identified trends that showed individuals with low self-efficacy, low motivation, risk-averse tendencies, and those who prefer to learn by processes and gather information selectively were the ones who faced the most challenges. Conclusion: The findings provide insights into how debuggers can be made more inclusive. They also highlight the need for continuous evaluation and adaptation of SE tools and practices to ensure they meet the needs of all users with diverse cognitive styles to ensure fairness.

Source: Information and Software Technology

DOI: 10.1016/j.infsof.2025.107932

Authors: Faith Culas, Amisha Singh, Atharva Arankalle, Priyanka Dhopade, Kelly Blincoe

Institution: University of Auckland

Language: English

Topic: Teaching and Learning Programming

Subfield: Computer Science Applications

Field: Computer Science

Domain: Physical Sciences

SDG: Reduced inequalities

Open Access status: hybrid

Estimated APC paid: $3,830