In this extended abstract, we present our approach to computing a safe subset-regression test-suite, given the current, and the updated versions of the compiled binaries of managed C# codebase; the architecture of our implementation ChiARTS, the algorithm, and argument for the safety of our approach.
In recent years, product line engineering has been used effectively in many industrial setups to create a large variety of products. One key aspect of product line engineering is to develop re-usable assets often referred to as a platform. Such software platforms are inherently complex due to requirements of providing diverse functionalities, thereby leading to combinatorial test data explosion problem while validating these platforms. In this paper, we present a combinatorial approach for testing varied features and data diversity present within the platform. The proposed solution effectively takes care of complex interdependencies among diversity features and generates only valid combinations for test scenario. We also developed a prototype tool based on our proposed approaches to automate the platform testing. As part of our case study, we have used our prototype to validate a software platform widely being used across Philips Medical Systems (PMS) products. Initial results confirm that our approach significantly improves the overall platform testing process by reducing testing effort and improve the quality of the platform by detecting all interaction faults.
The tridimensional structure of a protein is constrained or stabilized by some local interactions between distant residues of the protein, such as disulfide bonds, electrostatic interactions or hydrogen links. The in silico prediction of the disulfide connectivity has been widely studied: most results were based on few amino-acids around bonded cysteines, which we call local environments of cysteines. In order to evaluate the impact of such local information onto residue pairing, we propose a machine learning based protocol, independent from the type of contact, to detect affinities between local environments which would contribute to residues pairing. Finally, we experiment our protocol on proteins that feature disulfide or salt bridges. The results show that local environments contribute to the formation of salt bridges. However, results on disulfide bridges are not significantly positive with the class of linear functions used by the perceptron-type algorithm we propose.
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