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Using Evolutionary Coupling to Establish Relevance Links Between Tests and Code Units. A case study on fault localization

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arxiv 2203.11343 v1 pith:2WDUOSZO submitted 2022-03-21 cs.SE

classification cs.SE
keywords codetestcementfaultlocalizationtestsunitsinformation
verification ladder T0 review T1 audit T2 compute T3 formal
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Many software engineering techniques, such as fault localization, operate based on relevance relationships between tests and code. These relationships are often inferred through the use of dynamic test execution information (test execution traces) that approximate the link between relevant code units and asserted, by the tests, program behaviour. Unfortunately, in practice dynamic information is not always available due to the overheads introduced by the instrumentation or the nature of the production environments. To deal with this issue, we propose CEMENT, a static technique that automatically infers such test and code relationships given the projects' evolution. The key idea is that developers make relevant changes on test and code units at the same period of time, i.e., co-evolution of tests and code units reflects a probable link between them. We evaluate CEMENT on 15 open source projects and show that it indeed captures relevant links. Additionally, we perform a fault localization case study where we compare CEMENT with an existing Information Retrieval-based Fault Localization (IRFL) technique and show that it achieves comparable performance. A further analysis of our results reveals a small overlap between the faults successfully localized by the two approaches suggesting complementarity. In particular, out of the 39 successfully localized faults, two are common while CEMENT and IRFL localize 16 and 21. These results demonstrate that test and code evolutionary coupling can effectively support test and debugging activities.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. REACCEPT: Automated Co-evolution of Production and Test Code Based on Dynamic Validation and Large Language Models

    cs.SE 2024-11 reject novelty 6.0 of 10

    REACCEPT uses GPT-4 with retrieval and compile/test/coverage feedback to identify and rewrite outdated Java test code, reporting higher success than prior methods.

  2. Enhancing Software Maintenance: A Learning to Rank Approach for Co-changed Method Identification

    cs.SE 2024-11 conditional novelty 5.0 of 10

    A random forest learning-to-rank model using past co-change history and code features ranks co-changed methods with NDCG@5 of 0.84, but its edge over a historical frequency baseline is only 4.7%.

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