Prepared publications

Those papers are under review and will continue evolving in the future, any feedback will be greatly appreciated

DSpot: Test Amplification for Automatic Assessment of Computational Diversity (pdf)

In this work, we characterize a new form of software diver- sity: the existence of a set of variants that (i) all share the same API, (ii) all behave the same according to an input- output based specification and (iii) exhibit observable dif- ferences when they run outside the specified input space. We quantify computational diversity as the dissimilarity be- tween execution traces on inputs that are outside the speci- fied domain. Our technique relies on test amplification. We propose source code transformations on test cases to explore the input domain and systematically sense the observation domain. We run our experiments on 472 variants of 7 classes from open-source, large and thoroughly tested Java classes. Our test amplification multiplies by ten the number of input points in the test suite and is effective at detecting software diversity.

Automatic Software Diversity in the Light of Test Suites (pdf)

A few works address the challenge of automating software diversification, and they all share one core idea: using automated test suites to drive diversification. However, there is is lack of solid understanding of how test suites, programs and transformations interact one with another in this process. We explore this intricate interplay in the context of a specific diversification technique called “sosiefication”.
Sosiefication generates sosie programs, i.e., variants of a program in which some statements are deleted, added or replaced but still pass the test suite of the original program. Our investigation of the influence of test suites on sosiefication exploits the following observation: test suites cover the different regions of programs in very unequal ways. Hence, we hypothesize that sosie synthesis has different performances on a statement that is covered by one hundred test case and on a statement that is covered by a single test case. We synthesize 24583 sosies on 6 popular open-source Java programs. Our results show that there are two dimensions for diversification. The first one lies in the specification: the more test cases cover a statement, the more difficult it is to synthesize sosies. Yet, to our surprise, we are also able to synthesize sosies on highly tested statements (up to 600 test cases), which indicates an intrinsic property of the programs we study. The second dimension is in the code: we manually explore dozens of sosies and characterize new types of forgiving code regions that are prone to diversification.
https://github.com/DIVERSIFY-project/sosie-dataset
https://github.com/DIVERSIFY-project/sosie-results