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Prioritizing test cases for regression testing
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Source International Symposium on Software Testing and Analysis archive
Proceedings of the 2000 ACM SIGSOFT international symposium on Software testing and analysis table of contents
Portland, Oregon, United States
Pages: 102 - 112  
Year of Publication: 2000
ISBN:1-58113-266-2
Also published in ...
Authors
Sebastian Elbaum  Dept. of Computer Science & Engineering, University of Nebraska, Lincoln, NE
Alexey G. Malishevsky  Computer Science Dept., Oregon State Univ., Corvallis, OR
Gregg Rothermel  Computer Science Dept., Oregon State Univ., Corvallis, OR
Sponsor
SIGSOFT: ACM Special Interest Group on Software Engineering
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 11,   Downloads (12 Months): 114,   Citation Count: 22
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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.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
I. S. Association. Software Engineering Standards, volume 3 of Std. 1061: Standard for Software Quality Methodology. Institute of Electrical and Electronics Engineers, 1999 edition, 1992.
 
2
 
3
4
5
 
6
M. E. Delamaro and J. C. Maldonado. Proteum{A Tool for the Assessment ofTest Adequacy for C Programs. In Proc. of the Conf. on Performability in Computing Sys. (PCS 96), pages 79{95, July 1996.
 
7
R. A. DeMillo, R. J. Lipton, and F. G. Sayward. Hints on Test Data Selection: Help for the Practicing Programmer. Computer, 11(4):34{41, Apr. 1978.
 
8
S. Elbaum, A. G. Malishevsky, and G. Rothermel. Prioritizing test cases for regression testing. Technical Report 00-60-03, Oregon State University, Feb. 2000.
 
9
S. G. Elbaum and J. C. Munson. A standard for the measurement of C complexity attributes. Technical Report TR-CS-98-02, University of Idaho, Feb. 1998.
 
10
 
11
 
12
13
 
14
R. G. Hamlet. Testing programs with the aid of a compiler. IEEE Trans. Softw. Eng., SE-3(4):279{290, July 1977.
 
15
 
16
M. Harrold and G. Rothermel. Aristotle: A system for research on and development of program analysis based tools. Technical Report OSU-CISRC- 3/97-TR17, Ohio State University, Mar 1997.
 
17
 
18
T. M. Khoshgoftaar and J. C. Munson. Predicting software development errors using complexity metrics. J. on Selected Areas in Comm., 8(2):253{261, Feb. 1990.
 
19
J. C. Munson. Software measurement: Problems and practice. Annals of Softw. Eng., 1(1):255{285, 1995.
 
20
J. C. Munson, S. G. Elbaum, R. M. Karcich, and J. P. Wilcox. Software risk assessment through software measurement and modeling. In Proc. IEEE Aerospace Conf., pages 137{147, Mar. 1998.
 
21
A. P. Nikora and J. C. Munson. Software evolution and the fault process. In Proc. Twenty Third Annual Softw. Eng. Workshop, NASA/Goddard Space Flight Center, 1998.
22
23
 
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25
 
26
 
27
 
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CITED BY  22

Collaborative Colleagues:
Sebastian Elbaum: colleagues
Alexey G. Malishevsky: colleagues
Gregg Rothermel: colleagues