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Chianti: a tool for change impact analysis of java programs
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Source Conference on Object Oriented Programming Systems Languages and Applications archive
Proceedings of the 19th annual ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications table of contents
Vancouver, BC, Canada
SESSION: Verification and validation table of contents
Pages: 432 - 448  
Year of Publication: 2004
ISBN:1-58113-831-9
Also published in ...
Authors
Xiaoxia Ren  Rutgers University, Piscataway, NJ
Fenil Shah  IBM Software Group, Hawthorne, NY
Frank Tip  IBM T.J. Watson Research Center, Yorktown Heights, NY
Barbara G. Ryder  Rutgers University, Piscataway, NJ
Ophelia Chesley  Rutgers University, Piscataway, NJ
Sponsors
SIGPLAN: ACM Special Interest Group on Programming Languages
ACM: Association for Computing Machinery
SIGSOFT: ACM Special Interest Group on Software Engineering
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 37,   Downloads (12 Months): 172,   Citation Count: 35
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ABSTRACT

This paper reports on the design and implementation of Chianti, a change impact analysis tool for Java that is implemented in the context of the Eclipse environment. Chianti analyzes two versions of an application and decomposes their difference into a set of atomic changes. Change impact is then reported in terms of affected (regression or unit) tests whose execution behavior may have been modified by the applied changes. For each affected test, Chianti also determines a set of affecting changes that were responsible for the test's modified behavior. This latter step of isolating the changes that induce the failure of one specific test from those changes that only affect other tests can be used as a debugging technique in situations where a test fails unexpectedly after a long editing session. We evaluated Chianti on a year (2002) of CVS data from M. Ernst's Daikon system, and found that, on average, 52% of Daikon's unit tests are affected. Furthermore, each affected unit test, on average, is affected by only 3.95% of the atomic changes. These findings suggest that our change impact analysis is a promising technique for assisting developers with program understanding and debugging.


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.

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Bohner, S. A., and Arnold, R. S. An introduction to software change impact analysis. In Software Change Impact Analysis, S. A. Bohner and R. S. Arnold, Eds. IEEE Computer Society Press, 1996, pp. 1--26.
 
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Elbaum, S., Kallakuri, P., Malishevsky, A. G., Rothermel, G., and Kanduri, S. Understanding the effects of changes on the cost-effectiveness of regression testing techniques. Journal of Software Testing, Verification, and Reliability (2003). To appear.
 
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Ren, X., Shah, F., Tip, F., Ryder, B. G., Chesley, O., and Dolby, J. Chianti: A prototype change impact analysis tool for Java. Tech. Rep. DCS-TR-533, Rutgers University Department of Computer Science, September 2003.
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Thione, G. L. Detecting semantic conflicts in parallel changes, December 2002. Masters Thesis, Department of Electrical and Computer Engineering, University of Texas, Austin.
 
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Thione, G. L., and Perry, D. E. Parallel changes: Detecting semantic interference. Tech. Rep. ESEL-2003-DSI-1, Experimental Software Engineering Laboratory, University of Texas, Austin, September 2003.
 
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Tip, F. A survey of program slicing techniques. J. of Programming Languages 3, 3 (1995), 121--189.
 
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Tonella, P. Using a concept lattice of decomposition slices for program understanding and impact analysis. IEEE Trans. on Software Engineering 29, 6 (2003), 495--509.
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CITED BY  35

Collaborative Colleagues:
Xiaoxia Ren: colleagues
Fenil Shah: colleagues
Frank Tip: colleagues
Barbara G. Ryder: colleagues
Ophelia Chesley: colleagues