AT&T Labs Research - Ostrand, Thomas J.

Thomas Ostrand is a Principal Member of Technical Staff in the Information and Software Systems Research department of AT&T Labs. His current research interests include software test generation and evaluation, defect analysis and prediction, and tools for software development and testing.

Prior to joining AT&T, Tom was a member of the U.S. lab of Siemens Corporate Research in Princeton, NJ, and the software engineering lab of Sperry Univac in Blue Bell, PA.
He was also a member of the Computer Science Department of Rutgers University in New Brunswick, NJ.
Tom is a senior member of the ACM, and is a past Member-at-Large of the ACM/SIGSOFT Executive Committee. He has served as Program Chair and Steering Committee member of the International Symposium on Software Testing and Analysis, and of the PROMISE Conference on Predictive Models in Software Engineering. He is currently an associate editor of the Empirical Software Engineering journal, and is a past associate editor of IEEE Transactions on Software Engineering.

Tom will serve as a co-chair of the Industrial Program of the International Conference on Software Testing (ICST), to be held in Montreal, Canada in April 2012.

{Several studies have examined code churn as a variable for predicting faults in large software systems. High churn is usually associated with more faults appearing in code that has been changed frequently. We investigate the extent to which faults can be predicted by the degree of churn alone, whether other code characteristics occur together with churn, and which combinations of churn and other characteristics provide the best predictions. We also investigate different types of churn, including both additions to and deletions from code, as well as overall amount of change to code. We have mined the version control database of a large software system to collect churn and other software measures from 18 successive releases of the system. We examine the frequency of faults plotted against various code characteristics, and evaluate a diverse set of prediction models based on many different combinations of independent variables, including both absolute and relative churn. Churn measures based on counts of lines added, deleted, and modified are very effective for fault prediction. Individually, counts of adds and modifications outperform counts of deletes, while the sum of all three counts was most effective. However, these counts did not improve prediction accuracy relative to a model that included a simple count of the number of times that a file had been changed in the prior release. Including a measure of change in the prior release is an essential component of our fault prediction method. Various measures seem to work roughly equivalently. }

This version of the work is reprinted here with permission of IEEE for your personal use. Not for redistribution. The definitive version was published in Testing: Academic & Industrial Conference (TAIC 2011) , 2011-03-25

{Does the use of fault prediction models to help focus software testing resources and other development efforts to improve software reliability lead to discovery of different faults in the next release, or simply an improved process for finding the same faults that would be found if the models were not used? In this short paper, we describe the challenges involved in estimating effects for this sort of intervention and discuss ways to empirically answer that question and ways to assess any changes, if present. We present several experimental design options and discuss the pros and cons of each.}

(c) ACM, 2010. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Workshop on the Future of Software Engineering Research , 2010-11-07

{Software testing research has not kept up with modern software system designs and applications, and software engineering education falls short of providing students with the type of knowledge and training that other engineering specialties require. Testing researchers should pay more attention to areas that are currently relevant for practicing software developers, such as embedded systems, mobile devices, safety-critical systems and other modern paradigms, in order to provide usable results and techniques for practitioners. We identify a number of skills that every software engineering student and faculty should have learned, and also propose that education for future software engineers should include significant exposure to real systems, preferably through hands-on training via internships at software-producing firms. }

(c) ACM, 2010. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in The 6th International Conference on Predictive Models in Software Engineering , 2010-09-12

{We investigate whether files in a large system that are modified by an individual developer consistently contain either more or fewer faults than the average of all files in the system. The goal of the investigation is to determine whether information about which particular developer modified a file is able to improve defect predictions. We also continue an earlier study to evaluate the use of counts of the number of developers who modified a file as predictors of the file's future faultiness. The results from this preliminary study indicate that adding information to a model about which particular developer modified a file is not likely to improve defect predictions. The study is limited to a single large system, and its results may not hold more widely. The bug ratio is only one way of measuring the 'fault-proneness' of an individual programmer's coding, and we intend to investigate other ways of evaluating bug introduction by individuals. }

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