Concurrent programs are difficult to test due to their inherent non-determinism. To address this problem, testing often requires the exploration of thread schedules of a program; this can be time-consuming when applied to real-world programs. Software defect prediction has been used to help developers find faults and prioritize their testing efforts. Prior studies have used machine learning to build such predicting models based on designed features that encode the characteristics of programs. However, research has focused on sequential programs; to date, no work has considered defect prediction for concurrent programs, with program characteristics distinguished from sequential programs. In this paper, we present ConPredictor, an approach to predict defects specific to concurrent programs by combining both static and dynamic program metrics. Specifically, we propose a set of novel static code metrics based on the unique properties of concurrent programs. We also leverage additional guidance from dynamic metrics constructed based on mutation analysis. Our evaluation on four large open source projects shows that ConPredictor improved both within-project defect prediction and cross-project defect prediction compared to traditional features.
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Software engineering collaboratories (SEClabs) and collaboratories as a service (CaaS)
Novel research ideas require strong evaluations. Modern software
engineering research evaluation typically requires a set of benchmark
programs. Open source software repositories have provided a great
opportunity for researchers to find such programs for use in their
evaluations. Many tools/techniques have been developed to help
automate the curation of open source software. There has also been
encouragement for researchers to provide their research artifacts so
that other researchers can easily reproduce the results. We argue
that these two trends (i.e., curating open source software for research
evaluation and the providing of research artifacts) drive the need for
Software Engineer Collaboratories (SEClabs). We envision research
communities coming together to create SEClab instances, where
research artifacts can be made publicly available to other
researchers. The community can then vet such artifacts and make them
available as a service, thus turning the collaboratory into a
Collaboratory as a Service (CaaS). If our vision is realized, the
speed and transparency of research will drastically increase.
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- PAR ID:
- 10094198
- Date Published:
- Journal Name:
- Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering
- Page Range / eLocation ID:
- 760 to 764
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3236024.3264839
