CarFast: achieving higher statement coverage faster
Abstract
Test coverage is an important metric of software quality, since it indicates thoroughness of testing. In industry, test coverage is often measured as statement coverage. A fundamental problem of software testing is how to achieve higher statement coverage faster, and it is a difficult problem since it requires testers to cleverly find input data that can steer execution sooner toward sections of application code that contain more statements.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The causal discrimination score is a stronger measure of discrimination than group and apparent discrimination scores.
“Our causal discrimination score is a stronger measure of discrimination, as it seeks out causality in software, measuring the fraction of inputs for which changing specific input characteristics causes the output to change [67].”
From Fairness testing: testing software for discrimination (2017) · cites this paper as [67]
Software testing offers a unique opportunity to conduct causal experiments to determine statistical causality.
“Software testing offers a unique opportunity to conduct causal experiments to determine statistical causality [67]”
From Fairness testing: testing software for discrimination (2017) · cites this paper as [67]
Our causal discrimination score goes beyond prior work by measuring causality.
“Our causal discrimination score goes beyond prior work by measuring causality [67].”
From Fairness testing: testing software for discrimination (2017) · cites this paper as [67]
The citation lists haven’t been fetched yet
References 63 · Cited by 55
- Authors
- Park, S., Hossain, B. M. M., Hussain, I., Csallner, C., Grechanik, M., Taneja, K., Fu, C., & Xie, Q.
- Affiliations
- Georgia Institute of Technology (US)
- University of Illinois Chicago (US)
- The University of Texas at Arlington (US)
- North Carolina State University (US)
- Accenture (United States) (US)
- Venue
- Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering
- Publisher
- ACM
- Published
- 2012-11-11
- Pages
- 1-11
- OpenAlex
- W2121650870
- Cited by
- 55
- References
- 63
- Citations by year
- 2024: 2 · 2023: 2 · 2021: 4 · 2020: 2 · 2019: 6 · 2018: 11 · 2017: 6 · 2016: 6 · 2015: 8 · 2014: 8
- FWCI
- 3.55
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) (publishedVersion)
- Funders
- National Science Foundation (9.16E+33)
- Topics
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Software Engineering Methodologies
- Keywords
- test coverage, statement coverage, software testing, input data generation, test data selection
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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