J
James A. Jones
Researcher at University of California, Irvine
Publications - 57
Citations - 5080
James A. Jones is an academic researcher from University of California, Irvine. The author has contributed to research in topics: Software & Software system. The author has an hindex of 24, co-authored 55 publications receiving 4615 citations. Previous affiliations of James A. Jones include Georgia Institute of Technology & University of California.
Papers
More filters
Proceedings ArticleDOI
Empirical evaluation of the tarantula automatic fault-localization technique
James A. Jones,Mary Jean Harrold +1 more
TL;DR: The studies show that, on the same set of subjects, the Tarantula technique consistently outperforms the other four techniques in terms of effectiveness in fault localization, and is comparable in efficiency to the least expensive of the other five techniques.
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.
Journal ArticleDOI
Test-suite reduction and prioritization for modified condition/decision coverage
James A. Jones,Mary Jean Harrold +1 more
TL;DR: New algorithms for test-suite reduction and prioritization that can be tailored effectively for use with modified condition/decision coverage (MC/DC) adequate are presented.
Proceedings ArticleDOI
Regression test selection for Java software
Mary Jean Harrold,James A. Jones,Tongyu Li,Donglin Liang,Alessandro Orso,Maikel Pennings,Saurabh Sinha,S. Alexander Spoon,Ashish Gujarathi +8 more
TL;DR: A safe regression-test-selection technique that, based on the use of a suitable representation, handles the features of the Java language and also handles incomplete programs.
Proceedings ArticleDOI
Lightweight fault-localization using multiple coverage types
TL;DR: The empirical results show that the cost of fault localization using combinations of coverage is less than using any individual coverage type and closer to the best case (without knowing in advance which kinds of faults are present), and using inferred data-dependence coverage retains most of the benefits of combinations.