Bibliographic record
Abstract
Mutation 2000-A Symposium on Mutation TestingSoftware testing involves, among other activities, construction of test cases, execution of the program being tested against these test cases and observation of program behaviour to determine its acceptability.Although its use is hardly pervasive, mutation-based testing is one of the most fascinating and powerful techniques for software testing.It achieves these goals by requiring the tester to construct test cases that will distinguish the program under test from associated mutant versions, each of which contains a small syntactic deviation, representing a specific type of fault, from the program under test.Sponsored by SERC (Software Engineering Research Center: http://www.serc.net)and the IEEE Reliability Society, as well as having financial support from Telcordia Technologies (formerly Bellcore) and the National Science Foundation, Mutation 2000 was held in San Jose, California on the 6-7 October 2000.It was co-located with the International Conference on Software Maintenance (ICSM 2000) and the International Symposium on Software Reliability Engineering (ISSRE 2000).Mutation 2000 was the first event of its kind to bring together researchers and practitioners of mutation testing from all over the world.These individuals shared their experiences and insights on various practical and theoretical aspects of mutation testing.Of all the papers presented, three were selected for publication in this special issue.The first paper, by Kim, Clark and McDermid, investigates the effectiveness of object-oriented (OO) testing strategies using the mutation method.Test cases are generated by using three different OO test methods to validate an application that is a beta version of an IBM product written in Java.The fault detection effectiveness of these tests is compared in terms of their capability for killing traditional mutants generated by a version of the mutation tool Mothra for Java, as well as OO-specific mutants generated by class mutation.The next paper, by Ghosh and Mathur, describes a method called interface mutation, for testing software components using the information available from the description of a component's interface.The test adequacy criterion based on this interface mutation is compared with control flow-based coverage criteria for their relative effectiveness in revealing faults and the cost incurred in developing test cases.The third paper, by Vincenzi, Maldonado, Barbosa and Delamaro, is an empirical study on unit and integration testing strategies for C programs using mutation-based criteria.This paper describes an
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.018 | 0.029 |
| Insufficient payload (model declined to judge) | 0.011 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".