TY - GEN
T1 - Using model-based diagnosis to improve software testing
AU - Zamir, Tom
AU - Stern, Roni
AU - Kalech, Meir
N1 - Publisher Copyright:
Copyright © 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2014/1/1
Y1 - 2014/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84908156403&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84908156403
T3 - Proceedings of the National Conference on Artificial Intelligence
SP - 1135
EP - 1141
BT - Proceedings of the National Conference on Artificial Intelligence
PB - AI Access Foundation
T2 - 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
Y2 - 27 July 2014 through 31 July 2014
ER -