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A comprehensive study of code-removal patches in automated program repair

EMPIRICAL SOFTWARE ENGINEERING 2022年第4期27卷 1-43页

作者： Ginelli, Davide Martinez, Matias Mariani, Leonardo Monperrus, Martin Univ Milano Bicocca Milan Italy Univ Polytech Hauts de France Valenciennes France KTH Royal Inst Technol Stockholm Sweden

automatic program repair (APR) techniques can promisingly help reduce the cost of debugging. Many relevant APR techniques follow the generate-and-validate approach, that is, the faulty program is iteratively modified with different change operators and then validated with a test suite until a plausible patch is generated. In particular, Kali is a generate-and-validate technique developed to investigate the possibility of generating plausible patches by only removing code. Former studies show that indeed Kali successfully addressed several faults. This paper addresses the single and particular case of code-removal patches in automated program repair. We investigate the reasons and the scenarios that make their creation possible, and the relationship with patches implemented by developers. Our study reveals that code-removal patches are often insufficient to fix bugs, and proposes a comprehensive taxonomy of code-removal patches that provides evidence of the problems that may affect test suites, opening new opportunities for researchers in the field of automatic program repair.

关键词： automatic program repair Code-removal patches Software testing Debugging

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Evaluating Distance Measures for program repair 23

Evaluating Distance Measures for Program Repair

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19th Annual ACM Conference on International Computing Education Research V.1 (ICER)

作者： Koutcheme, Charles Sarsa, Sami Leinonen, Juho Haaranen, Lassi Hellas, Arto Aalto Univ Espoo Finland Univ Auckland Auckland New Zealand

ISBN: (纸本)9781450399760

Background and Context: Struggling with programming assignments while learning to program is a common phenomenon in programming courses around the world. Supporting struggling students is a common theme in Computing Education Research (CER), where a wide variety of support methods have been created and evaluated. An important stream of research here focuses on program repair, where methods for automatically fixing erroneous code are used for supporting students as they debug their code. Work in this area has so far assessed the performance of the methods by evaluating the closeness of the proposed fixes to the original erroneous code. The evaluations have mainly relied on the use of edit distance measures such as the sequence edit distance and there is a lack of research on which distance measure is the most appropriate. Objectives: Provide insight into measures for quantifying the distance between erroneous code written by a student and a proposed change. We conduct the evaluation in an introductory programming context, where insight into the distance measures can provide help in choosing a suitable metric that can inform which fixes should be suggested to novices. Method: A team of five experts annotated a subset of the Dublin dataset, creating solutions for over a thousand erroneous programs written by students. We evaluated how the prominent edit distance measures from the CER literature compare against measures used in Natural Language Processing (NLP) tasks for retrieving the experts' solutions from a pool of proposed solutions. We also evaluated how the expert-generated solutions compare against the solutions proposed by common program repair algorithms. The annotated dataset and the evaluation code are published as part of the work. Findings: Our results highlight that the ROUGE score, classically used for evaluating the performance of machine summarization tasks, performs well as an evaluation and selection metric for program repair. We also highlight the p

关键词： program repair automated program repair automatic program repair distance measures distance metrics ROUGE BLEU dataset bug fixing feedback natural language processing educational data mining computing education

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Multi-objective Optimization-based Bug-fixing Template Mining for Automated program repair 7

Multi-objective Optimization-based Bug-fixing Template Minin...

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37th IEEE/ACM International Conference on Automated Software Engineering (ASE)

作者： Kim, Misoo Kim, Youngkyoung Kim, Kicheol Lee, Eunseok Sungkyunkwan Univ Inst Software Convergence Suwon South Korea Sungkyunkwan Univ Dept Elect & Comp Engn Suwon South Korea Sungkyunkwan Univ Dept Software Suwon South Korea Sungkyunkwan Univ Coll Comp & Informat Suwon South Korea

ISBN: (纸本)9781450394758

Template-based automatic program repair (T-APR) techniques depend on the quality of bug-fixing templates. For such templates to be of sufficient quality for T-APR techniques to succeed, they must satisfy three criteria: applicability, fixability, and efficiency. Existing template mining approaches select templates based only on the first criteria, and are thus suboptimal in their performance. This study proposes a multi-objective optimization-based bug-fixing template mining method for T-APR in which we estimate template quality based on nine code abstraction tasks and three objective functions. Our method determines the optimal code abstraction strategy (i.e., the optimal combination of abstraction tasks) which maximizes the values of three objective functions and generates a final set of bug-fixing templates by clustering template candidates to which the optimal abstraction strategy is applied. Our preliminary experiment demonstrated that our optimized strategy can improve templates' applicability and efficiency by 7% and 146% over the existing mining technique, respectively. We therefore conclude that the multi-objective optimization-based template mining technique effectively finds high-quality bug-fixing templates.

关键词： automatic program repair Multi-objective optimization Bug-fixing template mining NSGA-II

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Accelerating Search-based program repair 11

Accelerating Search-based Program Repair

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11th IEEE International Conference on Software Testing, Verification and Validation (ICST)

作者： Mehne, Ben Yoshida, Hiroaki Prasad, Mukul R. Sen, Koushik Gopinath, Divya Khurshid, Sarfraz Univ Calif Berkeley Berkeley CA 94720 USA Fujitsu Labs Amer Inc Sunnyvale CA USA CMU Silicon Valley Moffett Field CA USA Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9781538650127

automatic program repair techniques offer the possibility of reducing, or even eliminating, the substantial manual effort that currently goes into the patching of software defects. However, current repair techniques take minutes or hours, to generate rather simple repairs, severely limiting their practical applicability. Search-based program repair represents a popular class of automatic repair techniques. Patch compilation and test case execution are the dominant contributors to runtime in this class of repair techniques. In this work we propose two complementary techniques, namely Location Selection and Test-Case Pruning, to improve the efficiency of search-based repair techniques. Location Selection reduces the number of repair candidates examined in arriving at a repair, thereby reducing the number of patch compilations as well as the overall number of test case evaluations during the repair process. Test-Case Pruning, on the other hand, optimizes the number of test cases executed per examined candidate. We implement the proposed techniques in the context of SPR, a state-of-the-art search-based repair tool, evaluate them on the GenProg benchmarks and observe that the proposed techniques provide a 3.9X speed-up, on average, without any degradation in repair quality.

关键词： automatic program repair Debugging Test selection

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Towards s/engineer/bot: Principles for program repair Bots 1

Towards s/engineer/bot: Principles for Program Repair Bots

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IEEE/ACM 1st International Workshop on Bots in Software Engineering (BotSE)

作者： van Tonder, Rijnard Le Goues, Claire Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781728122625

Of the hundreds of billions of dollars spent on developer wages, up to 25% accounts for fixing bugs. Companies like Google save significant human effort and engineering costs with automatic bug detection tools, yet automatically fixing them is still a nascent endeavour. Very recent work (including our own) demonstrates the feasibility of automatic program repair in practice. As automated repair technology matures, it presents great appeal for integration into developer workflows. We believe software bots are a promising vehicle for realizing this integration, as they bridge the gap between human software development and automated processes. We envision repair bots orchestrating automated refactoring and bug fixing. To this end, we explore what building a repair bot entails. We draw on our understanding of patch generation, validation, and real world software development interactions to identify six principles that bear on engineering repair bots and discuss related design challenges for integrating human workflows. Ultimately, this work aims to foster critical focus and interest for making repair bots a reality.

关键词： bots program transformation automation software quality refactoring automatic program repair

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Empirical Review of Java program repair Tools: A Large-Scale Experiment on 2,141 Bugs and 23,551 repair Attempts 2019

Empirical Review of Java Program Repair Tools: A Large-Scale...

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27th ACM Joint Meeting on European Software Engineering Conference (ESEC) / Symposium on the Foundations of Software Engineering (FSE)

作者： Durieux, Thomas Madeiral, Fernanda Martinez, Matias Abreu, Rui Univ Lisbon INESC ID Lisbon Portugal Univ Lisbon IST Lisbon Portugal Univ Fed Uberlandia Uberlandia MG Brazil Univ Polytech Hauts de France Valenciennes France

ISBN: (纸本)9781450355728

In the past decade, research on test-suite-based automatic program repair has grown significantly. Each year, new approaches and implementations are featured in major software engineering venues. However, most of those approaches are evaluated on a single benchmark of bugs, which are also rarely reproduced by other researchers. In this paper, we present a large-scale experiment using 11 Java test-suite-based repair tools and 2,141 bugs from 5 benchmarks. Our goal is to have a better understanding of the current state of automatic program repair tools on a large diversity of benchmarks. Our investigation is guided by the hypothesis that the repairability of repair tools might not be generalized across different benchmarks. We found that the 11 tools 1) are able to generate patches for 21% of the bugs from the 5 benchmarks, and 2) have better performance on Defects4J compared to other benchmarks, by generating patches for 47% of the bugs from Defects4J compared to 10-30% of bugs from the other benchmarks. Our experiment comprises 23,551 repair attempts, which we used to find causes of non-patch generation. These causes are reported in this paper, which can help repair tool designers to improve their approaches and tools.

关键词： automatic program repair patch generation benchmark overfitting

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Failure-Driven program repair 2019

Failure-Driven Program Repair

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27th ACM Joint Meeting on European Software Engineering Conference (ESEC) / Symposium on the Foundations of Software Engineering (FSE)

作者： Ginelli, Davide Univ Milano Bicocca DISCo Milan Italy

ISBN: (纸本)9781450355728

program repair techniques can dramatically reduce the cost of program debugging by automatically generating program fixes. Although program repair has been already successful with several classes of faults, it also turned out to be quite limited in the complexity of the fixes that can be generated. This Ph.D. thesis addresses the problem of cost-effectively generating fixes of higher complexity by investigating how to exploit failure information to directly shape the repair process. In particular, this thesis proposes Failure-Driven program repair, which is a novel approach to program repair that exploits its knowledge about both the possible failures and the corresponding repair strategies, to produce highly specialized repair tasks that can effectively generate non-trivial fixes.

关键词： automatic program repair software defects automatic debugging

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VarFix: Balancing Edit Expressiveness and Search Effectiveness in Automated program repair 2021

VarFix: Balancing Edit Expressiveness and Search Effectivene...

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29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE)

作者： Wong, Chu-Pan Santiesteban, Priscila Kastner, Christian Le Goues, Claire Carnegie Mellon Univ Pittsburgh PA 15213 USA Coe Coll Cedar Rapids IA 52402 USA

ISBN: (纸本)9781450385626

automatically repairing a buggy program is essentially a search problem, searching for code transformations that pass a set of tests. Various search strategies have been explored, but they either navigate the search space in an ad hoc way using heuristics, or systemically but at the cost of limited edit expressiveness in the kinds of supported program edits. In this work, we explore the possibility of systematically navigating the search space without sacrificing edit expressiveness. The key enabler of this exploration is variational execution, a dynamic analysis technique that has been shown to be effective at exploring many similar executions in large search spaces. We evaluate our approach on INTROCLASSJAVA and DE-FECTS4J, showing that a systematic search is effective at leveraging and combining fixing ingredients to find patches, including many high-quality patches and multi-edit patches.

关键词： automatic program repair variational execution

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Neural program repair : Systems, Challenges and Solutions 22

Neural Program Repair : Systems, Challenges and Solutions

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13th Asia-Pacific Symposium on Internetware (Internetware) - A Software Paradigm for Internet Computing

作者： Zhong, Wenkang Li, Chuanyi Ge, Jidong Luo, Bin Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China

ISBN: (纸本)9781450397803

Automated program repair (APR) aims to automatically fix bugs in the source code. Recently, with advances in Deep Learning (DL) field, there has been an increase of Neural program repair (NPR) studies that use neural networks to model the patch-generation process. NPR approaches have a significant benefit in applicability over prior APR techniques because they do not require any specifications (e.g., a test suite) when generating patches. For this reason, NPR has recently become a popular research topic. In this paper, We undertake a literature review of latest NPR systems to help interested readers understand advancements in this emerging field. We begin by introducing background information of NPR. Next, to make the various NPR systems more understandable, we split them into a four-phase pipeline and discuss various design choices for each phase. To investigate the motivations of different design choices, We further highlight a number of challenges and summarize corresponding solutions adopted by existing NPR systems. Finally, we suggest some intriguing directions for the future research.

关键词： automatic program repair Neural networks Software reliability

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Exception-Driven Fault Localization for Automated program repair 21

Exception-Driven Fault Localization for Automated Program Re...

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21st IEEE International Conference on Software Quality, Reliability and Security (QRS)

作者： Ginelli, Davide Riganelli, Oliviero Micucci, Daniela Mariani, Leonardo Univ Milano Bicocca Milan Italy

ISBN: (纸本)9781665458139

Automated program repair (APR) techniques typically exploit spectrum-based fault localization (SBFL) to identify the program locations that should be patched, making the effectiveness of APR techniques dependent on the effectiveness of fault localization. Indeed, results show that SBFL often does not localize faults accurately, hindering the effectiveness of APR. In this paper, we propose EXCEPT, a technique that addresses the localization problem by focusing on the semantics of failures rather than on the correlation between the executed statements and the failed tests, as SBFL does. We focus on failures due to exceptions and we exploit their type and source to localize and guess the faults. Experiments with 43 exception-raising faults from the Defects4J benchmark show that EXCEPT can perform better than Ochiai and ssFix.

关键词： automatic program repair fault localization SBFL exceptions

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