IEEE Transactions on Reliability· 2013Q1
The DStar Method for Effective Software Fault Localization
- 460citations
- Q1SCImago
- 2013year
Short summary
The DStar (D*) method automatically identifies suspicious code locations for debugging, outperforming 38 other techniques across 24 programs.
AI-generated from the title and abstract; the full text is not read.
Key points
- DStar (D*) method automatically suggests suspicious code locations for fault localization.
- The technique does not require prior information on program structure or semantics.
- D* outperformed 38 other fault localization techniques across 24 single- and multi-fault programs.
- The effectiveness of D* increases with its exponent parameter, then levels off.
AI-generated from the title and abstract; the full text is not read.
Abstract
Effective debugging is crucial to producing reliable software. Manual debugging is becoming prohibitively expensive, especially due to the growing size and complexity of programs. Given that fault localization is one of the most expensive activities in program debugging, there has been a great demand for fault localization techniques that can help guide programmers to the locations of faults. In this paper, a technique named DStar (D*) is proposed which can suggest suspicious locations for fault localization automatically without requiring any prior information on program structure or semantics. D* is evaluated across 24 programs, and is compared to 38 different fault localization techniques. Both single-fault and multi-fault programs are used. Results indicate that D* is more effective at locating faults than all the other techniques it is compared to. An empirical evaluation is also conducted to illustrate how the effectiveness of D* increases as the exponent * grows, and then levels off when the exponent * exceeds a critical value. Discussions are presented to support such observations.
The authors' abstract, as published at the source. IEEE Transactions on Reliability, 2013 · DOI ↗
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Field: Software
SoftwareComputer Science