Fault-localization using dynamic slicing and change impact analysis

Alves, Elton Rafael; Gligoric, Milos; Jagannath, Vilas; d’Amorim, Marcelo

doi:10.1109/ase.2011.6100114

Cited by 56 publications

(35 citation statements)

References 6 publications

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“…The basic statistical model that works for most functional bugs [4,6,17,19,22,23,33] is very useful for performance diagnosis too, but still leaves many performance problems uncovered; statistical models that consider the number of times a predicate is true in each run (e.g., the ∆LDA model) is needed for diagnosing performance problems.…”

Section: Discussionmentioning

confidence: 99%

“…Basic model Although the exact models used by previous work [4,19,22,23,33] differ from each other, they mostly follow the same principle. That is, they think a good failure predictor should be true for many failure runs and should be false or not-observed in many success runs.…”

Section: Statistical Model Designsmentioning

confidence: 99%

“…Most statistical debugging work [4,17,19,22,23,33] uses statistical models that only care about whether a predicate is true for at least once during a run. We consider these as basic models.…”

Section: Statistical Model Designsmentioning

confidence: 99%

“…In general, statistical debugging [4,6,17,19,22,23,33] is an approach that uses statistical machine learning techniques to help failure diagnosis. It usually works in two steps.…”

Section: Designmentioning

confidence: 99%

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Statistical debugging for real-world performance problems

SongLinhai

2014

SIGPLAN Not.

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Design and implementation defects that lead to inefficient computation widely exist in software. These defects are difficult to avoid and discover. They lead to severe performance degradation and energy waste during production runs, and are becoming increasingly critical with the meager increase of single-core hardware performance and the increasing concerns about energy constraints. Effective tools that diagnose performance problems and point out the inefficiency root cause is sorely needed.The state of the art of performance diagnosis is preliminary. Profiling can tell where computation resources are spent, but not where and why the resources are wasted. Performance-bug detectors can identify specific type of inefficient computation, but are not suited for diagnosing general performance problems. Effective failure diagnosis techniques, such as statistical debugging, have been proposed for functional bugs. However, whether they work for performance problems is still an open question.In this paper, we first conduct an empirical study to understand how performance problems are observed and reported by real-world users. Our study shows that statistical debugging is a natural fit for diagnosing performance problems, which are often observed through comparison-based approaches and reported together with both good and bad inputs. We then thoroughly investigate different design points in statistical debugging, including three different predicates and two different types of statistical models, to understand which design point works the best for performance diagnosis. Finally, we study how some unique nature of performance bugs allows sampling techniques to lower the overhead of run-time performance diagnosis without extending the diagnosis latency.

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Section: Discussionmentioning

confidence: 99%

Section: Statistical Model Designsmentioning

confidence: 99%

Section: Statistical Model Designsmentioning

confidence: 99%

“…In general, statistical debugging [4,6,17,19,22,23,33] is an approach that uses statistical machine learning techniques to help failure diagnosis. It usually works in two steps.…”

Section: Designmentioning

confidence: 99%

See 2 more Smart Citations

Statistical debugging for real-world performance problems

SongLinhai

2014

SIGPLAN Not.

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show abstract

“…In general, statistical debugging [4,6,20,22,29,30,40] is an approach that uses statistical machine learning techniques to help failure diagnosis. It usually works in two steps.…”

Section: Designmentioning

confidence: 99%

Statistical debugging for real-world performance problems

Song

2014

Proceedings of the 2014 ACM International Conference on Object Oriented Programming Systems Languages &Amp; Applications

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Design and implementation defects that lead to inefficient computation widely exist in software. These defects are difficult to avoid and discover. They lead to severe performance degradation and energy waste during production runs, and are becoming increasingly critical with the meager increase of single-core hardware performance and the increasing concerns about energy constraints. Effective tools that diagnose performance problems and point out the inefficiency root cause are sorely needed.The state of the art of performance diagnosis is preliminary. Profiling can identify the functions that consume the most computation resources, but can neither identify the ones that waste the most resources nor explain why. Performance-bug detectors can identify specific type of inefficient computation, but are not suited for diagnosing general performance problems. Effective failure diagnosis techniques, such as statistical debugging, have been proposed for functional bugs. However, whether they work for performance problems is still an open question.In this paper, we first conduct an empirical study to understand how performance problems are observed and reported by real-world users. Our study shows that statistical debugging is a natural fit for diagnosing performance problems, which are often observed through comparison-based approaches and reported together with both good and bad inputs. We then thoroughly investigate different design points in statistical debugging, including three different predicates and two different types of statistical models, to understand which design point works the best for performance diagnosis. Finally, we study how some unique nature of performance bugs allows sampling techniques to lower the over- * Shan is now with University of Chicago.

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Software Fault Localization: an Overview of Research, Techniques, and Tools

Wong

Gao²,

et al. 2023

Handbook of Software Fault Localization

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Fault-localization using dynamic slicing and change impact analysis

Cited by 56 publications

References 6 publications

Statistical debugging for real-world performance problems

Statistical debugging for real-world performance problems

Statistical debugging for real-world performance problems

Software Fault Localization: an Overview of Research, Techniques, and Tools

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