Software Fault Localization

Wong, W. Eric; Debroy, Vidroha

doi:10.1081/e-ese-120044231

Cited by 54 publications

(61 citation statements)

References 31 publications

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“…The first is to locate the fault in order to find where the bug exists. The second is to correct the fault in order to fix the located bug [11]. Much time is spent finding the locations of faults in such a process.…”

Section: Related Workmentioning

confidence: 99%

“…First, suspicious code is identified that may contain a fault. Next, the developer determines whether it actually contains a fault [11]. In this paper, fault localization refers to the task of finding candidates for faulty symbols (symbol name and address) by tracking the memory based on the suit of failed tests (binary source file, test case, and memory) from the HiL simulation.…”

Section: Introductionmentioning

confidence: 99%

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Fault Localization Method by Partitioning Memory Using Memory Map and the Stack for Automotive ECU Software Testing

2016

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Abstract:Recently, the usage of the automotive Electronic Control Unit (ECU) and its software in cars is increasing. Therefore, as the functional complexity of such software increases, so does the likelihood of software-related faults. Therefore, it is important to ensure the reliability of ECU software in order to ensure automobile safety. For this reason, systematic testing methods are required that can guarantee software quality. However, it is difficult to locate a fault during testing with the current ECU development system because a tester performs the black-box testing using a Hardware-in-the-Loop (HiL) simulator. Consequently, developers consume a large amount of money and time for debugging because they perform debugging without any information about the location of the fault. In this paper, we propose a method for localizing the fault utilizing memory information during black-box testing. This is likely to be of use to developers who debug automotive software. In order to observe whether symbols stored in the memory have been updated, the memory is partitioned by a memory map and the stack, thus the fault candidate region is reduced. A memory map method has the advantage of being able to finely partition the memory, and the stack method can partition the memory without a memory map. We validated these methods by applying these to HiL testing of the ECU for a body control system. The preliminary results indicate that a memory map and the stack reduce the possible fault locations to 22% and 19% of the updated memory, respectively.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fault Localization Method by Partitioning Memory Using Memory Map and the Stack for Automotive ECU Software Testing

2016

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“…These approaches differ in the way the failing and successful executions are obtained, the way they compare executions, and in the information they report to the user. A detailed survey about the differences among these approaches can be found in [16,18]. The main difference to our approach is that we do not execute the program and that we do not need a successful execution.…”

Section: Introductionmentioning

confidence: 99%

Flow-Sensitive Fault Localization

Christ

Ermis

Schäf³

et al. 2013

Lecture Notes in Computer Science

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Abstract. Identifying the cause of an error is often the most timeconsuming part in program debugging. Fault localization techniques can help to automate this task. Particularly promising are static proof-based techniques that rely on an encoding of error traces into trace formulas. By identifying irrelevant portions of the trace formula, the possible causes of the error can be isolated. One limitation of these approaches is that they do not take into account the control flow of the program and therefore miss common causes of errors, such as faulty branching conditions. This limitation is inherent to the way the error traces are encoded. In this paper, we present a new flow-sensitive encoding of error traces into trace formulas. The new encoding enables proof-based techniques to identify irrelevant conditional choices in an error trace and to include a justification for the truth value of branching conditions that are relevant for the localized cause of an error. We apply our new encoding to the fault localization technique based on error invariants and show that it produces more meaningful error explanations than previous approaches.

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“…Clustering algorithms aim to leverage this correspondence by grouping tests having similar execution profiles. The generated test clusterings are sampled in different ways to retrieve relevant tests to improve several software validation activities including observation-based testing [2]- [4], regression test selection [5], [6], prioritization [7], minimization [8] as well as fault localization [9]. This paper considers the use of clustering algorithms for software regression testing where a new version of the program has to be re-tested to ensure that it is functioning correctly.…”

Section: Introductionmentioning

confidence: 99%

Test Cluster Selection Using Cover Coefficients

Subramaniam¹,

Chundi²

2015

Software Engineering

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Clustering test profiles to retrieve relevant tests is a recurring theme in software validation. A novel clustering approach using a probabilistic notion of coverage among line-based test profiles is described. The approach automatically determines the number of clusters to generate a clustering and can potentially group together tests to execute a few distinct lines of code. A simple method to identify tests affected by program changes is developed and used to determine the retrieval effort needed for cluster-based retrieval of affected tests. The approach is applied to four unix utility programs from a popular testing benchmark. Our results show that comparing original and new clusterings with respect to test cases is a promising criterion for deciding the potential re-clustering points in software evolution.

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Software Fault Localization

Cited by 54 publications

References 31 publications

Fault Localization Method by Partitioning Memory Using Memory Map and the Stack for Automotive ECU Software Testing

Fault Localization Method by Partitioning Memory Using Memory Map and the Stack for Automotive ECU Software Testing

Flow-Sensitive Fault Localization

Test Cluster Selection Using Cover Coefficients

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