TY - GEN
T1 - Bridging the gap between the total and additional test-case prioritization strategies
AU - Zhang, Lingming
AU - Hao, Dan
AU - Zhang, Lu
AU - Rothermel, Gregg
AU - Mei, Hong
PY - 2013
Y1 - 2013
N2 - In recent years, researchers have intensively investigated various topics in test-case prioritization, which aims to re-order test cases to increase the rate of fault detection during regression testing. The total and additional prioritization strategies, which prioritize based on total numbers of elements covered per test, and numbers of additional (not-yet-covered) elements covered per test, are two widely-adopted generic strategies used for such prioritization. This paper proposes a basic model and an extended model that unify the total strategy and the additional strategy. Our models yield a spectrum of generic strategies ranging between the total and additional strategies, depending on a parameter referred to as the p value. We also propose four heuristics to obtain differentiated p values for different methods under test. We performed an empirical study on 19 versions of four Java programs to explore our results. Our results demonstrate that wide ranges of strategies in our basic and extended models with uniform p values can significantly outperform both the total and additional strategies. In addition, our results also demonstrate that using differentiated p values for both the basic and extended models with method coverage can even outperform the additional strategy using statement coverage.
AB - In recent years, researchers have intensively investigated various topics in test-case prioritization, which aims to re-order test cases to increase the rate of fault detection during regression testing. The total and additional prioritization strategies, which prioritize based on total numbers of elements covered per test, and numbers of additional (not-yet-covered) elements covered per test, are two widely-adopted generic strategies used for such prioritization. This paper proposes a basic model and an extended model that unify the total strategy and the additional strategy. Our models yield a spectrum of generic strategies ranging between the total and additional strategies, depending on a parameter referred to as the p value. We also propose four heuristics to obtain differentiated p values for different methods under test. We performed an empirical study on 19 versions of four Java programs to explore our results. Our results demonstrate that wide ranges of strategies in our basic and extended models with uniform p values can significantly outperform both the total and additional strategies. In addition, our results also demonstrate that using differentiated p values for both the basic and extended models with method coverage can even outperform the additional strategy using statement coverage.
UR - http://www.scopus.com/inward/record.url?scp=84881274508&partnerID=8YFLogxK
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U2 - 10.1109/ICSE.2013.6606565
DO - 10.1109/ICSE.2013.6606565
M3 - Conference contribution
AN - SCOPUS:84881274508
SN - 9781467330763
T3 - Proceedings - International Conference on Software Engineering
SP - 192
EP - 201
BT - 2013 35th International Conference on Software Engineering, ICSE 2013 - Proceedings
T2 - 2013 35th International Conference on Software Engineering, ICSE 2013
Y2 - 18 May 2013 through 26 May 2013
ER -