Enhancing Automated Program Repair with Deductive Verification

Fix pattern-based patch generation is a promising direction in Automated Program Repair (APR). Notably, it has been demonstrated to produce more acceptable and correct patches than the patches obtained with mutation operators through genetic programming. The performance of pattern-based APR systems, however, depends on the fix ingredients mined from fix changes in development histories. Unfortunately, collecting a reliable set of bug fixes in repositories can be challenging. In this paper, we propose to investigate the possibility in an APR scenario of leveraging code changes that address violations by static bug detection tools. To that end, we build the AVATAR APR system, which exploits fix patterns of static analysis violations as ingredients for patch generation. Evaluated on the Defects4J benchmark, we show that, assuming a perfect localization of faults, AVATAR can generate correct patches to fix 34/39 bugs. We further find that AVATAR yields performance metrics that are comparable to that of the closely-related approaches in the literature. While AVATAR outperforms many of the state-of-theart pattern-based APR systems, it is mostly complementary to current approaches. Overall, our study highlights the relevance of static bug finding tools as indirect contributors of fix ingredients for addressing code defects identified with functional test cases.Index Terms-Automated program repair, static analysis, fix pattern.

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“…8 Possible null pointer dereference. 9 Dead store to a local variable. 10 A private method is never called.…”

Section: Applying Avatar To Statically-detected Bugs In Defects4jmentioning

confidence: 99%

AVATAR: Fixing Semantic Bugs with Fix Patterns of Static Analysis Violations

Liu

Koyuncu

Kim

et al. 2019

2019 IEEE 26th International Conference on Software Analysis, Evolution and Reengineering (SANER)

124

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“…Third Experiment Although having a specification language based on separation logic allows us to precisely specify preconditions of the programs under test and generate valid test inputs, it could be non-trivial for ordinary users to use such a language. This problem has been recognized by the community and there have been multiple approaches to solve this problem [2,27,31,39]. One noticeable example which has made industrial impact is the Infer static analyzer [2], which infers preconditions of programs through bi-abduction [13].…”

Section: Rq1: Does Csf Generate Valid Test Inputs?mentioning

confidence: 99%

Concolic Testing Heap-Manipulating Programs

Pham

Phan

et al. 2019

Lecture Notes in Computer Science

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Concolic testing is a test generation technique which works effectively by integrating random testing generation and symbolic execution. Existing concolic testing engines focus on numeric programs. Heap-manipulating programs make extensive use of complex heap objects like trees and lists. Testing such programs is challenging due to multiple reasons. Firstly, test inputs for such programs are required to satisfy non-trivial constraints which must be specified precisely. Secondly, precisely encoding and solving path conditions in such programs are challenging and often expensive. In this work, we propose the first concolic testing engine called CSF for heap-manipulating programs based on separation logic. CSF effectively combines specification-based testing and concolic execution for test input generation. It is evaluated on a set of challenging heapmanipulating programs. The results show that CSF generates valid test inputs with high coverage efficiently. Furthermore, we show that CSF can be potentially used in combination with precondition inference tools to reduce the user effort.

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“…Mechtaev et al propose Angelix that can fix bugs using semantics analysis and program synthesis [21]. Subsequently, Le et al propose to use syntax-guided synthesis [15] and deductive verification [13] for program repair. However, even state-ofthe-art approaches can only fix a small number of bugs whose fixes span one or a few lines of code.…”

Section: Related Workmentioning

confidence: 99%

Recommending Code Changes for Automatic Backporting of Linux Device Drivers

Thung

et al. 2016

2016 IEEE International Conference on Software Maintenance and Evolution (ICSME)

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Device drivers are essential components of any operating system (OS). They specify the communication protocol that allows the OS to interact with a device. However, drivers for new devices are usually created for a specific OS version. These drivers often need to be backported to the older versions to allow use of the new device. Backporting is often done manually, and is tedious and error prone. To alleviate this burden on developers, we propose an automatic recommendation system to guide the selection of backporting changes. Our approach analyzes the version history for cues to recommend candidate changes. We have performed an experiment on 100 Linux driver files and have shown that we can give a recommendation containing the correct backport for 68 of the drivers. For these 68 cases, 73.5%, 85.3%, and 88.2% of the correct recommendations are located in the Top-1, Top-2, and Top-5 positions of the recommendation lists respectively. The successful cases cover various kinds of changes including change of record access, deletion of function argument, change of a function name, change of constant, and change of if condition. Manual investigation of failed cases highlights limitations of our approach, including inability to infer complex changes, and unavailability of relevant cues in version history.

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Enhancing Automated Program Repair with Deductive Verification

Cited by 34 publications

References 27 publications

AVATAR: Fixing Semantic Bugs with Fix Patterns of Static Analysis Violations

AVATAR: Fixing Semantic Bugs with Fix Patterns of Static Analysis Violations

Concolic Testing Heap-Manipulating Programs

Recommending Code Changes for Automatic Backporting of Linux Device Drivers

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