JOURNAL ARTICLE

Using automatically generated invariants for regression testing and bug localization

Abstract

We present Preambl, an approach that applies automatically generated invariants to regression testing and bug localization. Our invariant generation methodology is Precis, an automatic and scalable engine that uses program predicates to guide clustering of dynamically obtained path information. In this paper, we apply it for regression testing and for capturing program predicates information to guide statistical analysis based bug localization. We present a technique to localize bugs in paths of variable lengths. We are able to map the localized post-deployment bugs on a path to pre-release invariants generated along that path. Our experimental results demonstrate the efficacy of the use of PRECIS for regression testing, as well as the ability of Preambl to zone in on relevant segments of program paths.

Keywords:
Regression testing Computer science Cluster analysis Path (computing) Regression Regression analysis Scalability Data mining Invariant (physics) Program analysis Software bug Statistical hypothesis testing Artificial intelligence Machine learning Programming language Software Statistics Mathematics Software development Database

Metrics

4
Cited By
0.00
FWCI (Field Weighted Citation Impact)
21
Refs
0.13
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Software Testing and Debugging Techniques
Physical Sciences →  Computer Science →  Software
Software Engineering Research
Physical Sciences →  Computer Science →  Information Systems
Software Reliability and Analysis Research
Physical Sciences →  Computer Science →  Software

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