Int. J. Computing Science and Mathematics, Vol. 11, No. 4, 2020 357 Copyright © 2020 Inderscience Enterprises Ltd. Effective test data generation using probabilistic networks Farid Feyzi and Saeed Parsa* Department of Computer Engineering, Iran University of Science and Technology, Tehran, Iran Email: Farid_feyzi@comp.iust.ac.ir Email: Parsa@iust.ac.ir *Corresponding author Abstract: This paper presents a novel test data generation method called Bayes-TDG. It is based on principles of Bayesian networks (BNs) and provides the possibility of making inference from probabilistic data in the model to increase the prime path coverage ratio (PPCR) for a given program under test (PUT). In this regard, a new program structure-based probabilistic network, TDG-NET, is proposed that is capable of modelling the conditional dependencies among the program basic blocks (BBs) in one hand and conditional dependencies of the transitions between its BBs and input parameters on the other hand. To achieve failure-detection effectiveness, we propose a path selection strategy that works based on the predicted outcome of generated test cases. So, we mitigate the need for a human oracle, and the generated test suite could be directly used in fault localisation. Several experiments are conducted to evaluate the performance of Bayes-TDG. The results reveal that the method is promising and the generated test suite could be quite effective. Keywords: software testing; Bayesian net; test data generation; adaptive random testing; fault detection. Reference to this paper should be made as follows: Feyzi, F. and Parsa, S. (2020) ‘Effective test data generation using probabilistic networks’, Int. J. Computing Science and Mathematics, Vol. 11, No. 4, pp.357–371. Biographical notes: Farid Feyzi received his MS in Software Engineering from the Sharif University of Technology in 2012. He is currently a PhD candidate in the Department of Computer Engineering at Iran University of Science and Technology. His research focus is on developing statistical algorithms to improve software quality with an emphasis on statistical fault localisation and automated test data generation. Saeed Parsa received his BSc in Mathematics and Computer Science from the Sharif University of Technology, Iran, his MSc in Computer Science from the University of Salford in England, and his PhD in Computer Science from the University of Salford, England. He is an Associate Professor of Computer Science at the Iran University of Science and Technology. His research interests include software engineering, software testing and debugging and algorithms.