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Improving students’ testing skill through mutation testing with modified feedback and training
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DOI：10.1080/08993408.2026.2671830.png)
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Abstract
En 中文
Mutation testing (MT) is a powerful technique that evaluates test suite quality by introducing intentional faults (mutations) into code. In junior-level Data Structures and Algorithms (DSA) courses, students are tasked with developing programming projects with multi-week life cycles where MT could improve their test suites and coding skills. However, teaching students to effectively use MT is challenging. This study introduces a modified version of MT feedback combined with training content as an alternative to traditional code coverage for improving student test suites. We conducted a comparative analysis across three semesters that used different testing approaches: code coverage, MT with standard feedback from a widely used MT implementation (PIT), and MT with feedback modified and training to better support student understanding. We support our findings using 38 semi-structured interviews and quantitative analysis of IDE activity data and students’ code submissions. Initial implementation of standard PIT MT with a reduced set of mutation operators showed mixed results, as the stock feedback failed to meet student needs. We developed an improved approach incorporating additional documentation and training with practical examples, client-side support for MT, and improved feedback messages. The revised feedback and training was associated with positive results in student understanding and application of MT principles. This research provides a framework for effectively integrating MT in programming courses, contributing to improved student test suite development, better student code, and better project outcomes. The findings offer practical guidelines for instructors teaching testing in CS courses, bridging the gap between industry tools and educational needs.
Keywords:
CS education
testing
mutation analysis
code coverage
post-CS2
feedback