Applying classification techniques to remotely-collected program execution data

There is an increasing interest in techniques that support measurement and analysis of fielded software systems. One of the main goals of these techniques is to better understand how software actually behaves in the field. In particular, many of these techniques require a way to distinguish, in the field, failing from passing executions. So far, researchers and practitioners have only partially addressed this problem: they have simply assumed that program failure status is either obvious (i.e., the program crashes) or provided by an external source (e.g., the users). In this paper, we propose a technique for automatically classifying execution data, collected in the field, as coming from either passing or failing program runs. (Failing program runs are executions that terminate with a failure, such as a wrong outcome.) We use statistical learning algorithms to build the classification models. Our approach builds the models by analyzing executions performed in a controlled environment ...

• Type: Conference
• Year: 2005
• Where: SIGSOFT
• Authors: Murali Haran, Alan F. Karr, Alessandro Orso, Adam A. Porter, Ashish P. Sanil
• Keywords: Execution Data, Fielded Software Systems, Program Failure Status, SIGSOFT 2005, Software Engineering