An empirical investigation of the relationship between spectra differences and regression faults

Harrold, Mary Jean; Rothermel, Gregg; Kent, Sayre,; Wu, Rui; Liu, Yi

doi:10.1002/1099-1689(200009)10:3<171::aid-stvr209>3.0.co;2-j

Cited by 163 publications

(101 citation statements)

References 11 publications

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“…Thus, recording program spectra is light-weight, compared to other run-time methods for analyzing dynamic behavior (e.g., dynamic slicing [22]). Although we work with these so-called component-hit spectra, the approach outlined in this section easily generalizes to other types of program spectra [17].…”

Section: Program Spectramentioning

confidence: 99%

Architecture-Based Run-Time Fault Diagnosis

Casanova

Schmerl

Garlan

et al. 2011

Software Architecture

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Abstract. An important step in achieving robustness to run-time faults is the ability to detect and repair problems when they arise in a running system. Effective fault detection and repair could be greatly enhanced by run-time fault diagnosis and localization, since it would allow the repair mechanisms to focus adaptation effort on the parts most in need of attention. In this paper we describe an approach to run-time fault diagnosis that combines architectural models with spectrum-based reasoning for multiple fault localization. Spectrum-based reasoning is a lightweight technique that takes a form of trace abstraction and produces a list (ordered by probability) of likely fault candidates. We show how this technique can be combined with architectural models to support run-time diagnosis that can (a) scale to modern distributed software systems; (b) accommodate the use of black-box components and proprietary infrastructure for which one has neither a specification nor source code; and (c) handle inherent uncertainty about the probable cause of a problem even in the face of transient faults and faults that arise only when certain combinations of system components interact.

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Section: Program Spectramentioning

confidence: 99%

Architecture-Based Run-Time Fault Diagnosis

Casanova

Schmerl

Garlan

et al. 2011

Software Architecture

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“…The set of test cases are run over the faulty program and observations of how the program runs on each of the test cases are recorded as program spectra. A program spectrum [22], [39] is simply a set of data or values collected at runtime; each value could be a program state, or a counter or a flag for a program element. A test oracle is available to label whether a particular output or execution of a test case is correct or wrong.…”

Section: A Spectrum-based Fault Localizationmentioning

confidence: 99%

“…There have been various spectra proposed in the literature [1], [22], but we are particularly interested in block-hit program spectra, each of which consists of a set of flags to indicate whether each basic block is executed or not in each test case. 1 An example of block-hit program spectra is shown in Figure 1.…”

Section: A Spectrum-based Fault Localizationmentioning

confidence: 99%

Comprehensive evaluation of association measures for fault localization

Lucia

Jiang

et al. 2010

2010 IEEE International Conference on Software Maintenance

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Abstract-In statistics and data mining communities, there have been many measures proposed to gauge the strength of association between two variables of interest, such as odds ratio, confidence, Yule-Y, Yule-Q, Kappa, and gini index. These association measures have been used in various domains, for example, to evaluate whether a particular medical practice is associated positively to a cure of a disease or whether a particular marketing strategy is associated positively to an increase in revenue, etc. This paper models the problem of locating faults as association between the execution or non-execution of particular program elements with failures. There have been special measures, termed as suspiciousness measures, proposed for the task. Two state-of-the-art measures are Tarantula and Ochiai, which are different from many other statistical measures. To the best of our knowledge, there is no study that comprehensively investigates the effectiveness of various association measures in localizing faults. This paper fills in the gap by evaluating 20 wellknown association measures and compares their effectiveness in fault localization tasks with Tarantula and Ochiai. Evaluation on the Siemens programs show that a number of association measures perform statistically comparable as Tarantula and Ochiai.

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“…Fault localization computes fault candidates by looking at many executions, both passing and failing, as exemplified by [10,11,12,13,14,15,16,17]. Details of these techniques cannot be presented due to space limits.…”

Section: Related Workmentioning

confidence: 99%

Algorithms for Automatically Computing the Causal Paths of Failures

Sumner¹,

Zhang²

2009

Fundamental Approaches to Software Engineering

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Abstract.We have proposed an automated debugging technique that explains a failure by computing its causal path leading from the root cause to the failure. Given a failing execution, the technique first searches for a dynamic patch. Fine-grained execution comparison between the failing run and the patched run is performed to isolate the causal path. The comparison is enabled by precisely aligning the two executions. We herein propose and study two algorithms aiming at efficiency. We also evaluate the effectiveness and cost of our technique on a set of real bugs, including requirement bugs in which no a single or small set of statements can be blamed as the root cause. In such cases, understanding a failure is more important.

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An empirical investigation of the relationship between spectra differences and regression faults

Cited by 163 publications

References 11 publications

Architecture-Based Run-Time Fault Diagnosis

Architecture-Based Run-Time Fault Diagnosis

Comprehensive evaluation of association measures for fault localization

Algorithms for Automatically Computing the Causal Paths of Failures

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