An empirical investigation of program spectra

Harrold, Mary Jean; Rothermel, Gregg; Liu, Yi

doi:10.1145/277633.277647

Cited by 48 publications

(24 citation statements)

References 8 publications

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“…AMPLE works on a hypothesis first stated by Reps et al (1997) and later confirmed by Harrold et al (1998): faults correlate with differences in traces between a correct and a faulty run. A trace is a sequence of actions observed over the lifetime of a program.…”

Section: How Ample Workmentioning

confidence: 97%

Lightweight bug localization with AMPLE

Dallmeier

Lindig

Zeller

2005

Proceedings of the Sixth International Symposium on Automated Analysis-Driven Debugging

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AMPLE locates likely failure-causing classes by comparing method call sequences of passing and failing runs. A difference in method call sequences, such as multiple deallocation of the same resource, is likely to point to the erroneous class. Such sequences can be collected from arbitrary Java programs at low cost; comparing object-specific sequences predicts defects better than simply comparing coverage. AM-PLE comes as a plug-in for the Java IDE Eclipse that is automatically invoked as soon as a JUnit test fails.

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Section: How Ample Workmentioning

confidence: 97%

Lightweight bug localization with AMPLE

Dallmeier

Lindig

Zeller

2005

Proceedings of the Sixth International Symposium on Automated Analysis-Driven Debugging

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“…Harrold et al [13] give an overview of different types of program spectra. There exist several similarity coefficients to numerically express the degree of similarity, e.g.…”

Section: Related Workmentioning

confidence: 99%

Spectrum-Based Fault Localization for Spreadsheets: Influence of Correct Output Cells on the Fault Localization Quality

Hofer

2014

2014 IEEE International Symposium on Software Reliability Engineering Workshops

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Spreadsheets used in companies often contain several thousand formulas. The localization of faulty cells in such large spreadsheets could be time-consuming and frustrating. Spectrum-based fault localization (SFL) supports users in faster locating the faulty cell(s). However, SFL depends on the information the user provides. In this paper, we address three research questions in this context: (RQ1) Do spreadsheets contain correct output cells that positively or negatively influence the ranking of the faulty cells? (RQ2) If yes, is it possible to a-priori determine which correct output cells would positively influence the ranking? (RQ3) Is it possible to avoid a decreasing fault localization quality when adding more correct output cells? This paper shows that there exist correct output cells which positively or negatively influence the ranking. In particular, correct output cells with the largest cones positively influence the ranking of the faulty cell. Balancing the relation of correct and erroneous output cells by duplicating the cones of erroneous output cells improves the fault localization quality. I. INTRODUCTIONSpreadsheets are a well-known example of end-user programing. They are used in more than 95 % of the US companies [1], e.g., for financial reporting, data management, forward planning and investment decisions. Unfortunately, spreadsheets contain errors at an alarmingly high rate. Panko [2] estimates the human error rate when writing formulas to be 3 % to 5 %. Therefore, a spreadsheet with 100 formulas contains with a probability of 86.7 % to 99.4 % at least one fault. Even spreadsheets that build the basis for important decisions often contain faults. The European Spreadsheet Risk Interest Group (http://www.eusprig.org/horror-stories.htm) lists examples where spreadsheet faults led to financial losses.When an erroneous behavior is observed in a spreadsheet, the process of fault localization starts. Since spreadsheets that are used in companies are often large, containing several thousands of formulas, fault localization by manual inspection can be time-intensive and frustrating. In such a case, automatic fault localization techniques could help to automatically narrow down the search space.In previous work [3], we have discussed the application of spectrum-based fault localization (SFL) to spreadsheets.

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“…Program (component) activity is recorded in terms of program spectra [8]. This data is collected at run-time, and typically consists of a number of counters or flags for the different components of a program.…”

Section: A Basic Definitionsmentioning

confidence: 99%

Localizing Software Faults Simultaneously

Abreu

Zoeteweij

Gemund³

2009

2009 Ninth International Conference on Quality Software

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Current automatic diagnosis techniques are predominantly of a statistical nature and, despite typical defect densities, do not explicitly consider multiple faults, as also demonstrated by the popularity of the single-fault Siemens set. We present a logic reasoning approach, called Zoltar-M(ultiple fault), that yields multiple-fault diagnoses, ranked in order of their probability. Although application of Zoltar-M to programs with many faults requires further research into heuristics to reduce computational complexity, theory as well as experiments on synthetic program models and two multiple-fault program versions from the Siemens set show that for multiple-fault programs this approach can outperform statistical techniques, notably spectrum-based fault localization (SFL). As a side-effect of this research, we present a new SFL variant, called Zoltar-S(ingle fault), that is provably optimal for single-fault programs, outperforming all other variants known to date.

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An empirical investigation of program spectra

Cited by 48 publications

References 8 publications

Lightweight bug localization with AMPLE

Lightweight bug localization with AMPLE

Spectrum-Based Fault Localization for Spreadsheets: Influence of Correct Output Cells on the Fault Localization Quality

Localizing Software Faults Simultaneously

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