This week has seen two more achievements by some of my colleagues in the CS group at the GSSI.
Omar Inverso, postdoctoral researcher in the Computer Science group at the GSSI, has won a Silver Medal at the 2019 8th International Competition on Software Verification (SV-COMP 2019). Quoting from the paper at https://www.sosy-lab.org/
research/pub/2017-TACAS. Software_Verification_with_ Validation_of_Results.pdf,
As can be seen from the results of the competition at
Omar Inverso's Lazy-CSeq, a software tool for the automated analysis of complex concurrent programs, came second in the category ConcurrencySafety and was beaten only by a tool developed by a Chinese team from Tsinghua University, which is widely considered the best technical university in China. This is a remarkable achievement and continues Omar Inverso's success in that competition, where he won Gold and Silver medals in 2016, and Silver and Bronze medals in 2017, in the Concurrency category. It is also worth noting that Omar Inverso is developing Lazy-CSeq and related software-analysis tools alone, whereas competing tools are largely the result of a team effort.
Third-year Computer Science students Emilio Cruciani and Roberto Verdecchia have done it again! They have followed up on their ICSE 2018 paper with Breno Miranda(UFPE, Brazil) and Antonia Bertolino (ISTI - CNR, Italy) (see http://
processalgebra.blogspot.com/ 2017/12/first-year-computer- science-students-at.html for a few words on that achievement) with another paper, entitled "Scalable Approaches for Test Suite Reduction ", that has been accepted to the ICSE 2019 Technical Track (https://2019.icse- conferences.org/track/icse- 2019-Technical-Papers#event- overview). To put this achievement into perspective, ICSE is the premiere conference in software engineering. ICSE 2019 had 529 submissions, out of which 109 papers were accepted (with an acceptance rate of 21%).
The ICSE 2019 paper present an approach to test-suite reduction, which aims at decreasing software-regression-testing costs by selecting a representative subset from large-size test suites. It presents a family of novel, very efficient approaches for similarity-based test suite reduction that apply algorithms borrowed from the big-data domain together with smart heuristics for finding an evenly spread subset of test cases. The results of the experimental evaluation show that the approaches yield a fault detection loss comparable to state-of-the-art techniques, while providing huge gains in terms of efficiency.
Congratulations to Emilio, Omar and Roberto!