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277
Isolating Cause-Effect Chains from Computer Programs
, 2002
"... Consider the execution of a failing program as a sequence of program states. Each state induces the following state, up to the failure. Which variables and values of a program state are relevant for the failure? We show how the Delta Debugging algorithm isolates the relevant variables and values by ..."
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Cited by 253 (8 self)
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Consider the execution of a failing program as a sequence of program states. Each state induces the following state, up to the failure. Which variables and values of a program state are relevant for the failure? We show how the Delta Debugging algorithm isolates the relevant variables and values by systematically narrowing the state difference between a passing run and a failing run---by assessing the outcome of altered executions to determine wether a change in the program state makes a difference in the test outcome. Applying Delta Debugging to multiple states of the program automatically reveals the cause-effect chain of the failure---that is, the variables and values that caused the failure.
Empirical evaluation of the tarantula automatic fault-localization technique
- in Proceedings of the IEEE/ACM International Conference on Automated Software Engineering
, 2005
"... The high cost of locating faults in programs has motivated the development of techniques that assist in fault localization by automating part of the process of searching for faults. Empirical studies that compare these techniques have reported the relative effectiveness of four existing techniques o ..."
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Cited by 226 (7 self)
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The high cost of locating faults in programs has motivated the development of techniques that assist in fault localization by automating part of the process of searching for faults. Empirical studies that compare these techniques have reported the relative effectiveness of four existing techniques on a set of subjects. These studies compare the rankings that the techniques compute for statements in the subject programs and the effectiveness of these rankings in locating the faults. However, it is unknown how these four techniques compare with Tarantula, another existing fault-localization technique, although this technique also provides a way to rank statements in terms of their suspiciousness. Thus, we performed a study to compare the Tarantula technique with the four techniques previously compared. This paper presents our study—it overviews the Tarantula technique along with the four other techniques studied, describes our experiment, and reports and discusses the results. Our studies show that, on the same set of subjects, the Tarantula technique consistently outperforms the other four techniques in terms of effectiveness in fault localization, and is comparable in efficiency to the least expensive of the other four techniques.
Fault Localization with Nearest Neighbor Queries
, 2003
"... We present a method for performing fault localization using similar program spectra. Our method assumes the existence of a faulty run and a larger number of correct runs. It then selects according to a distance criterion the correct run that most resembles the faulty run, compares the spectra corres ..."
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Cited by 225 (2 self)
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We present a method for performing fault localization using similar program spectra. Our method assumes the existence of a faulty run and a larger number of correct runs. It then selects according to a distance criterion the correct run that most resembles the faulty run, compares the spectra corresponding to these two runs, and produces a report of "suspicious" parts of the program. Our method is widely applicable because it does not require any knowledge of the program input and no more information from the user than a classification of the runs as either "correct" or "faulty". To experimentally validate the viability of the method, we implemented it in a tool, WHITHER using basic block profiling spectra. We experimented with two different similarity measures and the Siemens suite of 132 programs with injected bugs. To measure the success of the tool, we developed a generic method for establishing the quality of a report. The method is based on the way an "ideal user" would navigate the program using the report to save effort during debugging. The best results we obtained were, on average, above 50%, meaning that our ideal user would avoid looking at half of the program.
Locating causes of program failures
- 27th International Conference on Software Engineering (ICSE
, 2005
"... Which is the defect that causes a software failure? By comparing the program states of a failing and a passing run, we can identify the state differences that cause the failure. However, these state differences can occur all over the program run. Therefore, we focus in space on those variables and v ..."
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Cited by 224 (8 self)
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Which is the defect that causes a software failure? By comparing the program states of a failing and a passing run, we can identify the state differences that cause the failure. However, these state differences can occur all over the program run. Therefore, we focus in space on those variables and values that are relevant for the failure, and in time on those moments where cause transitions occur—moments where new relevant variables begin being failure causes: “Initially, variable argc was 3; therefore, at shell sort(), variable a[2] was 0, and therefore, the program failed. ” In our evaluation, cause transitions locate the failureinducing defect twice as well as the best methods known so far.
Sober: statistical model-based bug localization
- In Proc. ESEC/FSE’05
, 2005
"... Automated localization of software bugs is one of the es-sential issues in debugging aids. Previous studies indicated that the evaluation history of program predicates may dis-close important clues about underlying bugs. In this paper, we propose a new statistical model-based approach, called SOBER, ..."
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Cited by 138 (12 self)
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Automated localization of software bugs is one of the es-sential issues in debugging aids. Previous studies indicated that the evaluation history of program predicates may dis-close important clues about underlying bugs. In this paper, we propose a new statistical model-based approach, called SOBER, which localizes software bugs without any prior knowledge of program semantics. Unlike existing statisti-cal debugging approaches that select predicates correlated with program failures, SOBER models evaluation patterns of predicates in both correct and incorrect runs respectively and regards a predicate as bug-relevant if its evaluation pat-tern in incorrect runs differs significantly from that in correct ones. SOBER features a principled quantification of the pat-tern difference that measures the bug-relevance of program predicates. We systematically evaluated our approach under the same setting as previous studies. The result demonstrated the power of our approach in bug localization: SOBER can help programmers locate 68 out of 130 bugs in the Siemens suite when programmers are expected to examine no more than 10 % of the code, whereas the best previously reported is 52 out of 130. Moreover, with the assistance of SOBER, we found two bugs in bc 1.06 (an arbitrary precision calcula-tor on UNIX/Linux), one of which has never been reported before.
Automated support for classifying software failure reports
- In ICSE
, 2003
"... This paper proposes automated support for classi ying reported software failures in order to facilitate prioritizing them and diagnosing their causes. A classification strategy is presented that involves the use of supervised and unsupervised pattern classification and multivariate visualization. Th ..."
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Cited by 115 (5 self)
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This paper proposes automated support for classi ying reported software failures in order to facilitate prioritizing them and diagnosing their causes. A classification strategy is presented that involves the use of supervised and unsupervised pattern classification and multivariate visualization. These techniques are applied to profiles o f failed executions in order to group together failures with the same or similar causes. The resulting classification is then used to assess the frequency and severity o f failures caused by particular defects and to help diagnose those defects. The results of applying the proposed classification strategy to failures of three large subject programs are reported. that the strategy can be effective. These results indicate
Gamma system: Continuous evolution of software after deployment
- In Proceedings of the international symposium on Software testing and analysis
, 2002
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Locating faulty code using failure-inducing chops
- In 20th IEEE/ACM International Conference on Automated Software Engineering (ASE 2005
, 2005
"... Software debugging is the process of locating and correcting faulty code. Prior techniques to locate faulty code either use program analysis techniques such as backward dynamic program slicing or exclusively use delta debugging to analyze the state changes during program execution. In this paper, we ..."
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Cited by 72 (11 self)
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Software debugging is the process of locating and correcting faulty code. Prior techniques to locate faulty code either use program analysis techniques such as backward dynamic program slicing or exclusively use delta debugging to analyze the state changes during program execution. In this paper, we present a new approach that integrates the potential of delta debugging algorithm with the benefit of forward and backward dynamic program slicing to narrow down the search for faulty code. Our approach is to use delta debugging algorithm to identify a minimal failure-inducing input, use this input to compute a forward dynamic slice and then intersect the statements in this forward dynamic slice with the statements in the backward dynamic slice of the erroneous output to compute a failure-inducing chop. We implemented our technique and conducted experiments with faulty versions of several programs from the Siemens suite to evaluate our technique. Our experiments show that failure-inducing chops can greatly reduce the size of search space compared to the dynamic slices without significantly compromising the capability to locate the faulty code. We also applied our technique to several programs with known memory related bugs such as buffer overflow bugs. The failure-inducing chop in several of these cases contained only 2 to 4 statements which included the code causing memory corruption.
Lightweight defect localization for java
- In ECOOP
, 2005
"... Abstract. A common method to localize defects is to compare the coverage of passing and failing program runs: A method executed only in failing runs, for instance, is likely to point to the defect. Some failures, though, come to be only through a specific sequence of method calls, such as multiple d ..."
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Cited by 70 (10 self)
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Abstract. A common method to localize defects is to compare the coverage of passing and failing program runs: A method executed only in failing runs, for instance, is likely to point to the defect. Some failures, though, come to be only through a specific sequence of method calls, such as multiple deallocation of the same resource. Such sequences can be collected from arbitrary Java programs at low cost; comparing object-specific sequences predicts defects better than simply comparing coverage. In a controlled experiment, our technique pinpointed the defective class in 39 % of all test runs. 1
An evaluation of similarity coefficients for software fault localization
- In: Proceedings of PRDC’06
"... Automated diagnosis of software faults can improve the ef-ficiency of the debugging process, and is therefore an im-portant technique for the development of dependable soft-ware. In this paper we study different similarity coeffi-cients that are applied in the context of a program spectral approach ..."
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Cited by 58 (11 self)
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Automated diagnosis of software faults can improve the ef-ficiency of the debugging process, and is therefore an im-portant technique for the development of dependable soft-ware. In this paper we study different similarity coeffi-cients that are applied in the context of a program spectral approach to software fault localization (single program-ming mistakes). The coefficients studied are taken from the systems diagnosis / automated debugging tools Pinpoint, Tarantula, and AMPLE, and from the molecular biology domain (the Ochiai coefficient). We evaluate these coeffi-cients on the Siemens Suite of benchmark faults, and as-sess their effectiveness in terms of the position of the actual fault in the probability ranking of fault candidates produced by the diagnosis technique. Our experiments indicate that the Ochiai coefficient consistently outperforms the coeffi-cients currently used by the tools mentioned. In terms of the amount of code that needs to be inspected, this coefficient improves 5 % on average over the next best technique, and up to 30 % in specific cases.
