Safe Memory-Leak Fixing for C Programs

Gao, Qing; Xiong, Yingfei; Mi, Yaqing; Zhang, Lu; Yang, Weikun; Zhou, Zhaoping; Xie, Bing; Mei, Hong

doi:10.1109/icse.2015.64

Cited by 56 publications

(29 citation statements)

References 51 publications

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“…Several approaches target at defect classes where the specification is complete, effectively avoiding the problem of weak test suites [11]. Typical defect classes include memory leaks [43], where the specification is semantically equivalent to the original program without leaks, concurrency bugs [44], [45], [46], where the specification is semantically equivalent to the original program without concurrency bugs, and configuration errors [47], where the specification can be interactively queried from the user. Though these approaches have a high precision, they target totally different defect classes compared with our work.…”

Section: Discussionmentioning

confidence: 99%

Precise Condition Synthesis for Program Repair

Xiong

Wang

Yan

et al. 2017

2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE)

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255

286

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Due to the difficulty of repairing defect, many research efforts have been devoted into automatic defect repair. Given a buggy program that fails some test cases, a typical automatic repair technique tries to modify the program to make all tests pass. However, since the test suites in real world projects are usually insufficient, aiming at passing the test suites often leads to incorrect patches. This problem is known as weak test suites or overfitting.In this paper we aim to produce precise patches, that is, any patch we produce has a relatively high probability to be correct. More concretely, we focus on condition synthesis, which was shown to be able to repair more than half of the defects in existing approaches. Our key insight is threefold. First, it is important to know what variables in a local context should be used in an "if" condition, and we propose a sorting method based on the dependency relations between variables. Second, we observe that the API document can be used to guide the repair process, and propose document analysis technique to further filter the variables. Third, it is important to know what predicates should be performed on the set of variables, and we propose to mine a set of frequently used predicates in similar contexts from existing projects.Based on the insight, we develop a novel program repair system, ACS, that could generate precise conditions at faulty locations. Furthermore, given the generated conditions are very precise, we can perform a repair operation that is previously deemed to be too overfitting: directly returning the test oracle to repair the defect. Using our approach, we successfully repaired 17 defects on four projects of Defects4J, which is the largest number of fully automatically repaired defects reported on the dataset so far. More importantly, the precision of our approach in the evaluation is 73.9%, which is significantly higher than previous approaches, which are usually less than 40%.

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Section: Discussionmentioning

confidence: 99%

Precise Condition Synthesis for Program Repair

Xiong

Wang

Yan

et al. 2017

2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE)

Self Cite

255

286

View full text Add to dashboard Cite

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“…Prophet is different from this technique, because Prophet directly learns a probabilistic model from successful human patches and does not rely on the existence of exact defect repair logic on Q&A pages. LeakFix: LeakFix statically analyzes the application source code and automatically inserts deallocation statements to fix memory leak errors in an applicaiton [9]. It guarantees that the new inserted deallocation will not interfere with normal executions.…”

Section: Automatic Patch Generationmentioning

confidence: 99%

Automatic patch generation by learning correct code

Long

Rinard

2016

SIGPLAN Not.

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We present Prophet, a novel patch generation system that works with a set of successful human patches obtained from open- source software repositories to learn a probabilistic, application-independent model of correct code. It generates a space of candidate patches, uses the model to rank the candidate patches in order of likely correctness, and validates the ranked patches against a suite of test cases to find correct patches. Experimental results show that, on a benchmark set of 69 real-world defects drawn from eight open-source projects, Prophet significantly outperforms the previous state-of-the-art patch generation system.

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“…It discards writing via null-references and returns zero as the result of divides-by-zero. LeakFix can correct memory leak by insert free-memory statements safely [9]. It analyzes memory allocations to guarantee that the patch will not interrupt normal execution of the program.…”

Section: Related Workmentioning

confidence: 99%

Experience report: How do techniques, programs, and tests impact automated program repair?

Kong

Zhang

Wong

et al. 2015

2015 IEEE 26th International Symposium on Software Reliability Engineering (ISSRE)

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Automated program repair can save tremendous manual efforts in software debugging. Therefore, a huge body of research efforts have been dedicated to design and implement automated program repair techniques. Among the existing program repair techniques, genetic-programming-based techniques have shown promising results. Recently, researchers found that random-search-based and adaptive program repair techniques can also produce effective results. In this work, we performed an extensive study for four program repair techniques, including genetic-programming-based, random-search-based, brute-forcebased and adaptive program repair techniques. Due to the extremely large time cost of the studied techniques, the study was performed on 153 bugs from 9 small to medium sized programs. In the study, we further investigated the impacts of different programs and test suites on effectiveness and efficiency of program repair techniques. We found that techniques that work well with small programs become too costly or ineffective when applied to medium sized programs. We also computed the false positive rates and discussed the ratio of the explored search space to the whole search space for each studied technique. Surprisingly, all the studied techniques except the random-searchbased technique are consistent with the 80/20 rule, i.e., about 80% of successful patches are found within the first 20% of search space.

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Safe Memory-Leak Fixing for C Programs

Cited by 56 publications

References 51 publications

Precise Condition Synthesis for Program Repair

Precise Condition Synthesis for Program Repair

Automatic patch generation by learning correct code

Experience report: How do techniques, programs, and tests impact automated program repair?

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