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Elaine Weyuker is an AT&T Fellow doing software engineering research. Prior to moving to AT&T she was a professor of computer science at NYU's Courant Institute of Mathematical Sciences. Her research interests currently focus on software fault prediction, software testing, and software metrics and measurement. In an earlier life, Elaine did research in Theory of Computation and is the co-author of a book "Computability, Complexity, and Languages" with Martin Davis and Ron Sigal.

Elaine is the recipient of the 2008 Anita Borg Institute Technical Leadership Award and 2007 ACM/SIGSOFT Outstanding Research Award. She is also a member of the US National Academy of Engineering, an IEEE Fellow, and an ACM Fellow and has received IEEE's Harlan Mills Award for outstanding software engineering research, Rutgers University 50th Anniversary Outstanding Alumni Award, and the AT&T Chairman's Diversity Award as well has having been named a Woman of Achievement by the YWCA. She is the chair of the ACM Women's Council (ACM-W) and a member of the Executive Committee of the Coalition to Diversify Computing.

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 Proceedings of the IEEE International Symposium on Empirical Software Engineering and Measurement. , 2011-09-22

{The Software Engineering Research community has slowly recognized that empirical studies are an important way of validating ideas and increasingly our community has stopped accepting the sufficiency of arguing that a smart person has come up with the idea and therefore it must be good. This has led to a flood of Software Engineering papers that contain at least some form of empirical study. However, not all empirical studies are created equal, and many may not even provide any useful information or value. We survey the gradual shift from essentially no empirical studies, to a small number of ones of questionable value, and look at what we need to do to insure that our empirical studies really contribute to the state of knowledge in the field. Thus we have the good, the bad, and the ugly. What are we as a community doing correctly? What are we doing less well than we should be because we either don't have the necessary artifacts or because the time and resources required to do ``the good'' is perceived to be too great? And where are we missing the boat entirely in terms of not addressing critical questions and often not even recognizing that these questions are central even if we don't know the answers. We look to see whether we can find some commonality in the projects that have really made the transition from research to widespread practice to see whether we can identify some common themes. }

{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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Awards
ACM Presidential Award 2010. The ACM Presidential Awards are given to leaders of Information Technology whose actions and achievements serve as paragons for the field. Recipients have demonstrated generosity, creativity and dedication to their respective missions.

Anita Borg Technical Leadership Award, 2008.

ACM SIGSOFT Outstanding Research Award, 2007.

ACM - Women in Computing Co-Chair, 2004.

AT&T Chairman's Diversity Award, 2004.

IEEE Harlan D. Mills, 2004.

IEEE Fellow, 2003. For contributions to the formal foundations of software testing.

National Academy, 2002.

YMCA Woman of Achievement Award, 2001.

AT&T Fellow, 2000. Testing software systems: Honored for seminal contributions having significant impact on testing software systems.

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