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Open AccessProceedings ArticleDOI

Prioritizing test cases for regression testing

TLDR
Can prioritization techniques be effective when aimed at specific modified versions; what tradeoffs exist between fine granularity and coarse granularity prioritized techniques; and can the incorporation of measures of fault proneness into prioritization technique improve their effectiveness?
Abstract
Test case prioritization techniques schedule test cases in an order that increases their effectiveness in meeting some performance goal. One performance goal, rate of fault detection, is a measure of how quickly faults are detected within the testing process; an improved rate of fault detection can provide faster feedback on the system under test, and let software engineers begin locating and correcting faults earlier than might otherwise be possible. In previous work, we reported the results of studies that showed that prioritization techniques can significantly improve rate of fault detection. Those studies, however, raised several additional questions: (1) can prioritization techniques be effective when aimed at specific modified versions; (2) what tradeoffs exist between fine granularity and coarse granularity prioritization techniques; (3) can the incorporation of measures of fault proneness into prioritization techniques improve their effectiveness? This paper reports the results of new experiments addressing these questions.

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Citations
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Journal ArticleDOI

Regression testing minimization, selection and prioritization: a survey

TL;DR: This paper surveys each area of minimization, selection and prioritization technique and discusses open problems and potential directions for future research.
Proceedings ArticleDOI

Visualization of test information to assist fault localization

TL;DR: A new technique that uses color to visually map the participation of each program statement in the outcome of the execution of the program with a test suite, consisting of both passed and failed test cases is presented.
Proceedings ArticleDOI

Automatically finding patches using genetic programming

TL;DR: A fully automated method for locating and repairing bugs in software that works on off-the-shelf legacy applications and does not require formal specifications, program annotations or special coding practices is introduced.
Journal ArticleDOI

Search Algorithms for Regression Test Case Prioritization

TL;DR: The paper addresses the problems of choice of fitness metric, characterization of landscape modality, and determination of the most suitable search technique to apply, and sheds light on the nature of the regression testing search space, indicating that it is multimodal.
Proceedings ArticleDOI

Are mutants a valid substitute for real faults in software testing

TL;DR: This paper investigates whether mutants are indeed a valid substitute for real faults, i.e., whether a test suite’s ability to detect mutants is correlated with its able to detect real faults that developers have fixed, and shows a statistically significant correlation between mutant detection and real fault detection, independently of code coverage.
References
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Book

Computers and Intractability: A Guide to the Theory of NP-Completeness

TL;DR: The second edition of a quarterly column as discussed by the authors provides a continuing update to the list of problems (NP-complete and harder) presented by M. R. Garey and myself in our book "Computers and Intractability: A Guide to the Theory of NP-Completeness,” W. H. Freeman & Co., San Francisco, 1979.
Book

An introduction to statistical methods and data analysis

R. Lyman Ott.
TL;DR: In this article, the Chi-square test of homogeneity of proportions is used to compare the proportions of different groups of individuals in a population to a single variable, and the Wilcoxon Signed-Rank Test is used for the comparison of different proportions.
Book

Software Testing Techniques

Boris Beizer
Journal ArticleDOI

Hints on Test Data Selection: Help for the Practicing Programmer

TL;DR: In many cases tests of a program that uncover simple errors are also effective in uncovering much more complex errors, so-called coupling effect can be used to save work during the testing process.
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