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Regression testing - running available tests after each project change - is widely practiced in industry. Despite its widespread use and importance, regression testing is a costly activity. Regression test selection (RTS) optimizes regression testing by selecting only tests affected by project changes. RTS has been extensively studied and several tools have been deployed in large projects. However, work on RTS over the last decade has mostly focused on languages with abstract computing machines(e.g., JVM). Meanwhile development practices (e.g., frequency of commits, testing frameworks, compilers) in C++ projects have dramatically changed and the way we should design and implement RTS tools and the benefits of those tools is unknown. We present a design and implementation of an RTS technique, dubbed RTS++, that targets projects written in C++, which compile to LLVM IR and use the Google Test testing framework. RTS++ uses static analysis of a function call graph to select tests. RTS++ integrates with many existing build systems, including AutoMake, CMake, and Make. We evaluated RTS++ on 11 large open-source projects, totaling 3,811,916 lines of code. To the best of our knowledge, this is the largest evaluation of an RTS technique for C++. We measured the benefits of RTS++compared to running all available tests (i.e., retest-all). Our results show that RTS++ reduces the number of executed tests and end-to-end testing time by 88% and 61% on average.
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An Exceptional Actor System (Functional Pearl)
The Glasgow Haskell Compiler is known for its feature-laden runtime system (RTS), which includes lightweight threads, asynchronous exceptions, and a slew of other features. Their combination is powerful enough that a programmer may complete the same task in many different ways --- some more advisable than others. We present a user-accessible actor framework hidden in plain sight within the RTS and demonstrate it on a classic example from the distributed systems literature. We then extend both the framework and example to the realm of dynamic types. Finally, we raise questions about how RTS features intersect and possibly subsume one another, and suggest that GHC can guide good practice by constraining the use of some features.
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- Award ID(s):
- 2145367
- PAR ID:
- 10519460
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400702983
- Page Range / eLocation ID:
- 32 to 43
- Format(s):
- Medium: X
- Location:
- Seattle WA USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3609026.3609728
