@inproceedings{1ef601cea0e948428ddab08984b4a994,
title = "Using model-based diagnosis to improve software testing",
abstract = "We propose a combination of AI techniques to improve software testing. When a test fails, a model-based diagnosis (MBD) algorithm is used to propose a set of possible explanations. We call these explanations diagnoses. Then, a planning algorithm is used to suggest further tests to identify the correct diagnosis. A tester preforms these tests and reports their outcome back to the MBD algorithm, which uses this information to prune incorrect diagnoses. This iterative process continues until the correct diagnosis is returned. We call this testing paradigm Test, Diagnose and Plan (TDP). Several test planning algorithms are proposed to minimize the number of TDP iterations, and consequently the number of tests required until the correct diagnosis is found. Experimental results show the benefits of using an MDP-based planning algorithms over greedy test planning in three benchmarks.",
author = "Tom Zamir and Roni Stern and Meir Kalech",
note = "Publisher Copyright: Copyright {\textcopyright} 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.; 28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014 ; Conference date: 27-07-2014 Through 31-07-2014",
year = "2014",
month = jan,
day = "1",
language = "الإنجليزيّة",
series = "Proceedings of the National Conference on Artificial Intelligence",
publisher = "AI Access Foundation",
pages = "1135--1141",
booktitle = "Proceedings of the National Conference on Artificial Intelligence",
address = "الولايات المتّحدة",
}