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Propagating frugal user feedback through closeness of code dependencies to improve IR-based traceability recovery

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Abstract

Traceability recovery captures trace links among different software artifacts (e.g., requirements and code) when two artifacts cover the same part of system functionalities. These trace links provide important support for developers in software maintenance and evolution tasks. Information Retrieval (IR) is now the mainstream technique for semi-automatic approaches to recover candidate trace links based on textual similarities among artifacts. The performance of IR-based traceability recovery is evaluated by the ranking of relevant traces in the generated lists of candidate links. Unfortunately, this performance is greatly hindered by the vocabulary mismatch problem between different software artifacts. To address this issue, a growing body of enhancing strategies based on user feedback is proposed to adjust the calculated IR values of candidate links after the user verifies part of these links. However, the improvement brought by this kind of strategies requires a large amount of user feedback, which could be infeasible in practice. In this paper, we propose to improve IR-based traceability recovery by propagating a small amount of user feedback through the closeness analysis on call and data dependencies in the code. Specifically, our approach first iteratively asks users to verify a small set of candidate links. The collected frugal feedback is then composed with the quantified functional similarity for each code dependency (called closeness) and the generated IR values to improve the ranking of unverified links. An empirical evaluation based on nine real-world systems with three mainstream IR models shows that our approach can outperform five baseline approaches by using only a small amount of user feedback.

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Notes

  1. http://www.CoEST.org

  2. https://agile.csc.ncsu.edu/iTrust/wiki/doku.php

  3. Java Virtual Machine Tool Interface, https://docs.oracle.com/javase/8/docs/platform/jvmti/jvmti.html

  4. https://github.com/huiAlex/CLUSTER-Prime

  5. https://agile.csc.ncsu.edu/iTrust/wiki/doku.php

  6. https://github.com/bardsoftware/ganttproject

  7. https://github.com/apache/maven

  8. https://github.com/apache/pig

  9. https://github.com/infinispan/infinispan

  10. https://github.com/kiegroup/drools

  11. https://github.com/apache/derby

  12. http://www.seamframework.org/Seam2.html

  13. https://github.com/apache/groovy

  14. https://gradle.org/

  15. https://github.com/gousiosg/java-callgraph

  16. https://www.atlassian.com/software/jira

  17. https://github.com/

  18. https://issues.redhat.com/browse/JBSEAM-4810

  19. https://issues.apache.org/jira/browse/DERBY-5840

  20. https://issues.redhat.com/browse/ISPN-5553

  21. https://issues.apache.org/jira/browse/DERBY-2583

  22. https://issues.apache.org/jira/browse/DERBY-2569

  23. https://issues.apache.org/jira/browse/DERBY-1478

  24. https://issues.apache.org/jira/browse/PIG-1429

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Acknowledgements

This work is funded by the National Natural Science Foundation of China (Grant Nos. 61690204 and 61802173), the general program of the State Key Laboratory for Novel Software Technology (Grant Nos. ZZKT2021B05), the Collaborative Innovation Center of Novel Software Technology and Industrialization, the German Ministry of Education and Research (BMBF) grant: 01IS16003B and by DFG grant: MA 5030/3-1, and funded by the Austrian Science Fund (FWF), grand no. P31989, and by the Austrian COMET K1-Centre Pro2Future of the Austrian Research Promotion Agency (FFG) with funding from the Austrian ministries BMVIT and BMDW, and the Province of Upper Austria.

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Authors and Affiliations

  1. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

    Hui Gao, Hongyu Kuang, Xiaoxing Ma, Hao Hu & Jian Lü

  2. Fakultät für Informatik und Automatisierung, Technische Universität, Ilmenau, Germany

    Patrick Mäder

  3. Institute for Software Systems Engineering, Johannes Kepler University, Linz, Austria

    Alexander Egyed

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  1. Hui Gao
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  2. Hongyu Kuang
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  3. Xiaoxing Ma
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  4. Hao Hu
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Correspondence to Hongyu Kuang.

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Communicated by: Federica Sarro and Foutse Khomh

This article belongs to the Topical Collection: International Conference on Program Comprehension (ICPC)

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Gao, H., Kuang, H., Ma, X. et al. Propagating frugal user feedback through closeness of code dependencies to improve IR-based traceability recovery. Empir Software Eng 27, 41 (2022). https://doi.org/10.1007/s10664-021-10091-5

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