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Auflistung nach Autor:in "Sharma, Arnab"

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  • Konferenzbeitrag
    Property-Driven Black-Box Testing of Numeric Functions
    (Software Engineering 2023, 2023) Sharma, Arnab; Melnikov, Vitalik; Hüllermeier, Eyke; Wehrheim, Heike
    In this work, we propose a property-driven testing mechanism to perform unit testing of functions performing numerical computations. Our approach, similar to the property-based testing technique, allows the tester to specify the requirements to check. Unlike property-based testing, the specification is then used to generate test cases in a targeted manner. Moreover, our approach works as a black-box testing tool, i.e. it does not require knowledge about the internals of the function under test. Therefore, besides on programmed numeric functions, we also apply our technique to machine-learned regression models. The experimental evaluation on a number of case studies shows the effectiveness of our testing approach.
  • Konferenzbeitrag
    Testing Balancedness of ML Algorithms
    (Software Engineering and Software Management 2019, 2019) Sharma, Arnab; Wehrheim, Heike
    With the increased application of machine learning (ML) algorithms to decision-making processes, the question of fairness of such algorithms came into the focus. Fairness testing aims at checking whether a classifier as “learned” by an ML algorithm on some training data is biased in the sense of discriminating against some of the attributes (e.g. gender or age). Fairness testing thus targets the prediction phase in ML, not the learning phase. In our approach, we investigate fairness for the learning phase. Our definition of fairness is based on the idea that the learner should treat all data in the training set equally, disregarding issues like names or orderings of features or orderings of data instances. We term this property balanced data usage. We have developed a (metamorphic) testing approach called TiLe for checking balanced data usage and report on some experiments of using TiLe to check classifiers from the scikit-learn library for balancedness.