Repairing crashes in Android apps

Tan, Shin Hwei; Dong, Zhao; Gao, Xiang; Roychoudhury, Abhik

doi:10.1145/3180155.3180243

Cited by 62 publications

(26 citation statements)

References 54 publications

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“…Additionally, there are only a few research-oriented tools that can automatically repair specific types of API misuses. For instance, consider those that repair incorrect error handling [26], [83], [84].…”

Section: Api-misuse Detectorsmentioning

confidence: 99%

Evaluating Automatic Program Repair Capabilities to Repair API Misuses

Kechagia

Mechtaev

Sarro

et al. 2022

IIEEE Trans. Software Eng.

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API misuses are well-known causes of software crashes and security vulnerabilities. However, their detection and repair is challenging given that the correct usages of (third-party) APIs might be obscure to the developers of client programs. This paper presents the first empirical study to assess the ability of existing automated bug repair tools to repair API misuses, which is a class of bugs previously unexplored. Our study examines and compares 14 Java test-suite-based repair tools (11 proposed before 2018, and three afterwards) on a manually curated benchmark (APIREPBENCH) consisting of 101 API misuses. We develop an extensible execution framework (APIARTY) to automatically execute multiple repair tools. Our results show that the repair tools are able to generate patches for 28% of the API misuses considered. While the 11 less recent tools are generally fast (the median execution time of the repair attempts is 3.87 minutes and the mean execution time is 30.79 minutes), the three most recent are less efficient (i.e., 98% slower) than their predecessors. The tools generate patches for API misuses that mostly belong to the categories of missing null check, missing value, missing exception, and missing call. Most of the patches generated by all tools are plausible (65%), but only few of these patches are semantically correct to human patches (25%). Our findings suggest that the design of future repair tools should support the localisation of complex bugs, including different categories of API misuses, handling of timeout issues, and ability to configure large software projects. Both APIREPBENCH and APIARTY have been made publicly available for other researchers to evaluate the capabilities of repair tools on detecting and fixing API misuses.

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Section: Api-misuse Detectorsmentioning

confidence: 99%

Evaluating Automatic Program Repair Capabilities to Repair API Misuses

Kechagia

Mechtaev

Sarro

et al. 2022

IIEEE Trans. Software Eng.

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“…Unlike the (many) other behavioral repair approaches in the existing literature [3], [48], [53], [54], [55], SAPFIX uses three different oracles to asses the quality and correctness of a fix: test cases from Facebook's CI, crash triggering sequences of UI events (similar to the work of Tan et al [56]), and human reviewers. One novelty of our work derives directly from our industrial deployment; we are able to rely on expert engineers to act as the final arbiter of correctness in each case of a deployed repair.…”

Section: Automated Fault Fixing (Repair)mentioning

confidence: 99%

“…Our SAPFIX approach targets Android NPEs and draws on templates automatically learned from human testers. Previously, Tan et al [56] studied a set of Android crashes from Github to identify a set of 8 mutation operators that are often used by Android developers to fix bugs. One of their mutation operators is 'Missing Null Check', which is similar to our NPE mutation operators in the mutation fix strategy.…”

Section: Automated Fault Fixing (Repair)mentioning

confidence: 99%

SapFix: Automated End-to-End Repair at Scale

Marginean

Bader

Chandra

et al. 2019

2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)

115

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“…具体来说, 这种方法首先通过从栈踪迹中提取信息, 并根据这些信息检索问答网站得到一系列 Q&A 页面, 通过分析所得到的 Q&A 页面, 抽取这些页面问题贴中的错误代码和回答帖中的修复代码, 进而生成编辑脚本, 生成候选补丁. Tan 等 [62] 提出了一种针对 Android 应用的崩溃修复方法 Droix, 该方法利用总结出的 17 个错误原因提出了 8 个修复模式, 并结合基于搜索的方法逐步迭代进化, 直到产生出满足测试要求的补丁. 实验证明 Droix 能够找到 24 个真实崩溃中的 15 个, 并且生成的补丁质量较高.…”

Section: 基于搜索的方法基于搜索的修复方法通过 "生成 -验证" 模式在特定的搜索空间内通过启发式的算法生成补丁并在配套unclassified