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Where were the repair ingredients for Defects4j bugs?

Exploring the impact of repair ingredient retrieval on the performance of 24 program repair systems

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Abstract

A significant body of automated program repair research has built approaches under the redundancy assumption. Patches are then heuristically generated by leveraging repair ingredients (change actions and donor code) that are found in code bases (either the buggy program itself or big code). For example, common change actions (i.e., fix patterns) are frequently mined offline and serve as an important ingredient for many patch generation engines. Although the repetitiveness of code changes has been studied in general, the literature provides little insight into the relationship between the performance of the repair system and the source code base where the change actions were mined. Similarly, donor code is another important repair ingredient to concretize patches guided by abstract patterns. Yet, little attention has been paid to where such ingredients can actually be found. Through a large scale empirical study on the execution results of 24 repair systems evaluated on real-world bugs from Defects4J, we provide a comprehensive view on the distribution of repair ingredients that are relevant for these bugs. In particular, we show that (1) a half of bugs cannot be fixed simply because the relevant repair ingredient is not available in the search space of donor code; (2) bugs that are correctly fixed by literature tools are mostly addressed with shallow change actions; (3) programs with little history of changes can benefit from mining change actions in other programs; (4) parts of donor code to repair a given bug can be found separately at different search locations; (5) bug-triggering test cases are a rich source for donor code search.

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Data Availability

All relevant artifacts of our study are publicly available at:

https://github.com/DehengYang/repair-ingredients.

Notes

  1. ReturnStatement, IfStatement, etc.

  2. The revision history may not be able to explore for some projects but most APR studies (Martinez et al. 2014; Pan et al. 2009; Barr et al. 2014; Nguyen et al. 2013; Liu et al. 2018; Zhong and Su 2015) assume that the revision history is available. The revision history in our study is also available as Defects4J bugs are all collected from real-world and large-scale projects.

  3. https://github.com/AutoProRepair/PatchParser.git

  4. https://github.com/program-repair/defects4j-dissection/blob/master/defects4j-patch.md

  5. In contrast with the mining of code change actions, most APR systems scan only the production code of a program for donor code.

  6. This number is different from 169 shown in Fig. 6 since the revisions related to Chart-11 and 26 are not available to explore as stated in the above paragraphs.

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Acknowledgements

This research was supported by the Open Project Program of the State Key Laboratory of Mathematical Engineering and Advanced Computing (No. 2020A06) and the Open Project Program of the Key Laboratory of Safety-Critical Software (Nanjing University of Aeronautics and Astronautics), Ministry of Industry and Information Technology (No. XCA20026), the National Key R&D Program of China (No. 2020AAA0107704), the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1A5A1021944), the National Defense Basic Scientific Research Program (No. WDZC20205500308), the Fundamental Research Funds for the Central Universities (Nos. 2021CDJKYJH032, 2020CDCGRJ037, 2020CDCGRJ072) and the National Natural Science Foundation of China (Nos. 61872445, 61672529), as well as the funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 949014).

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Authors and Affiliations

  1. National University of Defense Technology, Changsha, China

    Deheng Yang & Xiaoguang Mao

  2. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Kui Liu

  3. State Key Laboratory of Mathematical Engineering and Advanced Computing, Wuxi, China

    Kui Liu

  4. Key Laboratory of Safety-Critical Software (Nanjing University of Aeronautics and Astronautics), Ministry of Industry and Information Technology, Nanjing, China

    Kui Liu

  5. Kyungpook National University, Daegu, South Korea

    Dongsun Kim

  6. Sabanci University, Istanbul, Turkey

    Anil Koyuncu

  7. SnT, University of Luxembourg, Luxembourg City, Luxembourg

    Kisub Kim, Haoye Tian, Jacques Klein & Tegawendé F. Bissyandé

  8. Chongqing University, Chongqing, China

    Yan Lei

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Correspondence to Kui Liu.

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Yang, D., Liu, K., Kim, D. et al. Where were the repair ingredients for Defects4j bugs?. Empir Software Eng 26, 122 (2021). https://doi.org/10.1007/s10664-021-10003-7

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