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http://user.it.uu.se/~jarst116/slides/week4.pdf

Graph-Based Algorithms for Boolean Function Manipulation

Software fault localization, the act of identifying the locations of faults in a program, is widely recognized to be one of the most tedious, time consuming, and expensive – yet equally critical – activities in program debugging. Due to the increasing scale and complexity of software today, manually locating faults when failures occur is rapidly becoming infeasible, and consequently, there is a strong demand for techniques that can guide software developers to the locations of faults in a program with minimal human intervention. This demand in turn has fueled the proposal and development of a broad spectrum of fault localization techniques, each of which aims to streamline the fault localization process and make it more effective by attacking the problem in a unique way. In this article, we catalog and provide a comprehensive overview of such techniques and discuss key issues and concerns that are pertinent to software fault localization as a whole.

A Survey on Software Fault Localization

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.

/pdf/automatic-patch-generation-by-learning-correct-code-2y44ih20iy.pdf

Automatic patch generation by learning correct code

Since debugging is a time-consuming activity, automated program repair tools such as GenProg have garnered interest A recent study revealed that the majority of GenProg repairs avoid bugs simply by deleting functionality We found that SPR, a state-of-the-art repair tool proposed in 2015, still deletes functionality in their many "plausible" repairs Unlike generate-and-validate systems such as GenProg and SPR, semantic analysis based repair techniques synthesize a repair based on semantic information of the program While such semantics-based repair methods show promise in terms of quality of generated repairs, their scalability has been a concern so far In this paper, we present Angelix, a novel semantics-based repair method that scales up to programs of similar size as are handled by search-based repair tools such as GenProg and SPR This shows that Angelix is more scalable than previously proposed semantics based repair methods such as SemFix and DirectFix Furthermore, our repair method can repair multiple buggy locations that are dependent on each other Such repairs are hard to achieve using SPR and GenProg In our experiments, Angelix generated repairs from large-scale real-world software such as wireshark and php, and these generated repairs include multi-location repairs We also report our experience in automatically repairing the well-known Heartbleed vulnerability

/pdf/angelix-scalable-multiline-program-patch-synthesis-via-1loaty358w.pdf

Angelix: scalable multiline program patch synthesis via symbolic analysis

Automated program repair recently received considerable attentions, and many techniques on this research area have been proposed. Among them, two genetic-programming-based techniques, GenProg and Par, have shown the promising results. In particular, GenProg has been used as the baseline technique to check the repair effectiveness of new techniques in much literature. Although GenProg and Par have shown their strong ability of fixing real-life bugs in nontrivial programs, to what extent GenProg and Par can benefit from genetic programming, used by them to guide the patch search process, is still unknown. To address the question, we present a new automated repair technique using random search, which is commonly considered much simpler than genetic programming, and implement a prototype tool called RSRepair. Experiment on 7 programs with 24 versions shipping with real-life bugs suggests that RSRepair, in most cases (23/24), outperforms GenProg in terms of both repair effectiveness (requiring fewer patch trials) and efficiency (requiring fewer test case executions), justifying the stronger strength of random search over genetic programming. According to experimental results, we suggest that every proposed technique using optimization algorithm should check its effectiveness by comparing it with random search.

/pdf/the-strength-of-random-search-on-automated-program-repair-29yxr9h46x.pdf

The strength of random search on automated program repair

Slice-based statistical fault localization

Most techniques for automated program repair use test cases to validate the effectiveness of the produced patches. The validation process can be time-consuming especially when the object programs ship with either lots of test cases or some long-running test cases. To alleviate the cost for testing, we first introduce regression test prioritization insight into the area of automated program repair, and present a novel prioritization technique called FRTP with the goal of reducing the number of test case executions in the repair process. Unlike most existing prioritization techniques frequently requiring additional cost for gathering previous test executions information, FRTP iteratively extracts that information just from the repair process, and thus incurs trivial performance lose. We also built a tool called TrpAutoRepair, which implements our FRTP technique and has the ability of automatically repairing C programs. To evaluate TrpAutoRepair, we compared it with GenProg, a state-of-the-art tool for automated C program repair. The experiment on the 5 subject programs with 16 real-life bugs provides evidence that TrpAutoRepair performs at least as good as GenProg in term of success rate, in most cases (15/16), TrpAutoRepair can significantly improve the repair efficiency by reducing efficiently the test case executions when searching a valid patch in the repair process.

Efficient Automated Program Repair through Fault-Recorded Testing Prioritization

Many techniques on automated fault localization (AFL) have been introduced to assist developers in debugging. Prior studies evaluate the localization technique from the viewpoint of developers: measuring how many benefits that developers can obtain from the localization technique used when debugging. However, these evaluation approaches are not always suitable, because it is difficult to quantify precisely the benefits due to the complex debugging behaviors of developers. In addition, recent user studies have presented that developers working with AFL do not correct the defects more efficiently than ones working with only traditional debugging techniques such as breakpoints, even when the effectiveness of AFL is artificially improved. In this paper we attempt to propose a new research direction of developing AFL techniques from the viewpoint of fully automated debugging including the program repair of automation, for which the activity of AFL is necessary. We also introduce the NCP score as the evaluation measurement to assess and compare various techniques from this perspective. Our experiment on 15 popular AFL techniques with 11 subject programs shipping with real-life field failures presents the evidence that these AFL techniques performing well in prior studies do not have better localization effectiveness according to NCP score. We also observe that Jaccard has the better performance over other techniques in our experiment.

Using automated program repair for evaluating the effectiveness of fault localization techniques

Test-based automated program repair has been a prolific field of research in software engineering in the last decade. Many approaches have indeed been proposed, which leverage test suites as a weak, but affordable, approximation to program specifications. Although the literature regularly sets new records on the number of benchmark bugs that can be fixed, several studies increasingly raise concerns about the limitations and biases of state-of-the-art approaches. For example, the correctness of generated patches has been questioned in a number of studies, while other researchers pointed out that evaluation schemes may be misleading with respect to the processing of fault localization results. Nevertheless, there is little work addressing the efficiency of patch generation, with regard to the practicality of program repair. In this paper, we fill this gap in the literature, by providing an extensive review on the efficiency of test suite based program repair. Our objective is to assess the number of generated patch candidates, since this information is correlated to (1) the strategy to traverse the search space efficiently in order to select sensical repair attempts, (2) the strategy to minimize the test effort for identifying a plausible patch, (3) as well as the strategy to prioritize the generation of a correct patch. To that end, we perform a large-scale empirical study on the efficiency, in terms of quantity of generated patch candidates of the 16 open-source repair tools for Java programs. The experiments are carefully conducted under the same fault localization configurations to limit biases. Eventually, among other findings, we note that: (1) many irrelevant patch candidates are generated by changing wrong code locations; (2) however, if the search space is carefully triaged, fault localization noise has little impact on patch generation efficiency; (3) yet, current template-based repair systems, which are known to be most effective in fixing a large number of bugs, are actually least efficient as they tend to generate majoritarily irrelevant patch candidates.

/pdf/on-the-efficiency-of-test-suite-based-program-repair-a-1gtk1aheft.pdf

Xiaoguang Mao

Papers

The strength of random search on automated program repair

Slice-based statistical fault localization

Efficient Automated Program Repair through Fault-Recorded Testing Prioritization

Using automated program repair for evaluating the effectiveness of fault localization techniques

On the efficiency of test suite based program repair: A Systematic Assessment of 16 Automated Repair Systems for Java Programs