An empirical study of static program slice size

Binkley, David; Gold, Nicolas; Harman, Mark

doi:10.1145/1217295.1217297

Cited by 78 publications

(79 citation statements)

References 68 publications

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“…Horwitz et al (1990) extend the technique to inter-procedural slicing. However, the typical size of a static slice for a program can be one-third of the program (Binkley et al, 2007). It may not be useful to present such a large piece of code for developers to look for faulty statements.…”

Section: Program Slicingmentioning

confidence: 99%

Non-parametric statistical fault localization

Zhang

Chan

Tse

et al. 2011

Journal of Systems and Software

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Fault localization is a major activity in program debugging. To automate this time-consuming task, many existing fault-localization techniques compare passed executions and failed executions, and suggest suspicious program elements, such as predicates or statements, to facilitate the identification of faults. To do that, these techniques propose statistical models and use hypothesis testing methods to test the similarity or dissimilarity of proposed program features between passed and failed executions. Furthermore, when applying their models, these techniques presume that the feature spectra come from populations with specific distributions. The accuracy of using a model to describe feature spectra is related to and may be affected by the underlying distribution of the feature spectra, and the use of a (sound) model on inapplicable circumstances to describe real-life feature spectra may lower the effectiveness of these fault-localization techniques. In this paper, we make use of hypothesis testing methods as the core concept in developing a predicate-based fault-localization framework. We report a controlled experiment to compare, within our framework, the efficacy, scalability, and efficiency of applying three categories of hypothesis testing methods, namely, standard non-parametric hypothesis testing methods, standard parametric hypothesis testing methods, and debugging-specific parametric testing methods. We also conduct a case study to compare the effectiveness of the winner of these three categories with the effectiveness of 33 existing statement-level fault-localization techniques. The experimental results show that the use of non-parametric hypothesis testing methods in our proposed predicatebased fault-localization model is the most promising.

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Section: Program Slicingmentioning

confidence: 99%

Non-parametric statistical fault localization

Zhang

Chan

Tse

et al. 2011

Journal of Systems and Software

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“…We compute interprocedural static slices backward from the crash point in the innermost stack frame; intraprocedural slices work backward from the crash point in each function in the crash stack. All interprocedural slices are callstack-sensitive [9,18,24].…”

Section: B Analysis Effectivenessmentioning

confidence: 99%

Lightweight control-flow instrumentation and postmortem analysis in support of debugging

Ohmann

Liblit

2016

Autom Softw Eng

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Abstract-Debugging is difficult and costly. As a human programmer looks for a bug, it would be helpful to see a complete trace of events leading to the point of failure. Unfortunately, full tracing is simply too slow to use in deployment, and may even be impractical during testing.We aid post-deployment debugging by giving programmers additional information about program activity shortly before failure. We use latent information in post-failure memory dumps, augmented by low-overhead, tunable run-time tracing. Our results with a realistically-tuned tracing scheme show low enough overhead (0-5%) to be used in production runs. We demonstrate several potential uses of this enhanced information, including a novel postmortem static slice restriction technique and a reduced view of potentially-executed code. Experimental evaluation shows our approach to be very effective, such as shrinking stack-sensitive interprocedural static slices by 49-78% in larger applications.

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“…Java is the most popular programming language, in both open source [18] and industrial software development [19]; however, accuracy and scalability of Java program slicing techniques have not yet been investigated. Binkley et al [20] evaluate program slicing for C/C++. They compare slices obtained with various configurations of CodeSurfer [21].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Backward Slicing Techniques for Java

Kashima

Ishio

Inoue

2015

IEICE Trans. Inf. ^|^ Syst.

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SUMMARYProgram slicing is an important approach for debugging, program comprehension, impact analysis, etc. There are various program slicing techniques ranging from the lightweight to the more accurate but heavyweight. Comparative analyses are important for selecting the most appropriate technique. This paper presents a comparative study of four backward program slicing techniques for Java. The results show the scalability and precision of these techniques. We develop guidelines that indicate which slicing techniques are appropriate for different situations, based on the results.

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An empirical study of static program slice size

Cited by 78 publications

References 68 publications

Non-parametric statistical fault localization

Non-parametric statistical fault localization

Lightweight control-flow instrumentation and postmortem analysis in support of debugging

Comparison of Backward Slicing Techniques for Java

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