Identifying Testing Requirements for Modified Software

Throughout its lifetime, software must be changed for many reasons, such as bug fixing, performance tuning, and code restructuring. Testing modified software is the main activity performed to gain confidence that changes behave as they are intended and do not have adverse effects on the rest of the software. A fundamental problem of testing evolving software is determining whether test suites adequately exercise changes and, if not, providing suitable guidance for generating new test inputs that target the modified behavior. Existing techniques evaluate the adequacy of test suites based only on control- and data-flow testing criteria. They do not consider the effects of changes on program states and, thus, are not sufficiently strict to guarantee that the modified behavior is exercised. Also, because of the lack of this guarantee, these techniques can provide only limited guidance for generating new test inputs. This research has developed techniques that will assist testers in testing evolving software and provide confidence in the quality of modified versions. In particular, this research has developed a technique to identify testing requirements that ensure that the test cases satisfying them will result in different program states at preselected parts of the software. This research has also developed supporting techniques for identifying testing requirements. Such techniques include (1) a differencing technique, which computes differences and correspondences between two software versions and (2) two dynamic-impact-analysis techniques, which identify parts of software that are likely affected by changes with respect to a set of executions.Ph.D.Committee Chair: Dr. Harrold, Mary Jean; Committee Co-Chair: Dr. Orso, Alessandro; Committee Member: Dr. Hatcliff, John; Committee Member: Dr. Manolios, Panagiotis; Committee Member: Dr. Pande, Santos

ABSTRACT Leveraging Field Data for Impact Analysis and Regression Testing

Software products are often released with missing functionality, errors, or incompatibilities that may result in failures, inferior performances, or user dissatisfaction. In previous work, we presented the Gamma approach, which facilitates remote analysis and measurement of deployed software and permits gathering of program-execution data from the field. In this paper, we investigate the use of the Gamma approach to support and improve two fundamental tasks performed by software engineers during maintenance: impact analysis and regression testing. We present a new approach that leverages field data to perform these two tasks. The approach is efficient in that the kind of field data that we consider require limited space and little instrumentation. We also present a set of empirical studies that we performed, on a real subject and on a real user population, to evaluate the approach. The results of the studies show that the use of field data is effective and, for the cases considered, can considerably affect the results of dynamic analyses

A differencing algorithm for object-oriented programs

During software evolution, information about changes between different versions of a program is useful for a number of software engineering tasks. For many of these tasks, a purely syntactic differencing may not provide enough information for the task to be performed effectively. This problem is especially relevant in the case of object-oriented software, for which a syntactic change can have subtle and unforeseen effects. In this paper, we present a technique for comparing object-oriented programs that identifies both differences and correspondences between two versions of a program. The technique is based on a representation that handles object-oriented features and, thus, can capture the behavior of object-oriented programs. We also present JDIFF, a tool that implements the technique for Java programs, and empirical results that show the efficiency and effectiveness of the technique on a real program. 1

ABSTRACT Efficient and Precise Dynamic Impact Analysis Using Execute-After Sequences

As software evolves, impact analysis estimates the potential effects of changes, before or after they are made, by identifying which parts of the software may be affected by such changes. Traditional impact-analysis techniques are based on static analysis and, due to their conservative assumptions, tend to identify most of the software as affected by the changes. More recently, researchers have begun to investigate dynamic impact-analysis techniques, which rely on dynamic, rather than static, information about software behavior. Existing dynamic impact-analysis techniques are either very expensive—in terms of execution overhead or amount of dynamic information collected—or imprecise. In this paper, we present a new technique for dynamic impact analysis that is almost as efficient as the most efficient existing technique and is as precise as the most precise existing technique. The technique is based on a novel algorithm that collects (and analyzes) only the essential dynamic information required for the analysis. We discuss our technique, prove its correctness, and present a set of empirical studies in which we compare our new technique with two existing techniques, in terms of performance and precision

An Empirical Comparison of Dynamic Impact Analysis Algorithms

Impact analysis --- determining the potential effects of changes on a software system --- plays an important role in software engineering tasks such as maintenance, regression testing, and debugging. In previous work, two new dynamic impact analysis techniques, CoverageImpact and PathImpact, were presented. These techniques perform impact analysis based on data gathered about program behavior relative to specific inputs, such as inputs gathered from field data, operational profile data, or test-suite executions. Due to various characteristics of the algorithms they employ, CoverageImpact and PathImpact are expected to differ in terms of cost and precision; however, there have been no studies to date examining the extent to which such differences may emerge in practice. Since cost-precision tradeoffs may play an important role in technique selection and further research, we wished to examine these tradeoffs. We therefore designed and performed an empirical study, comparing the execution and space costs of the techniques, as well as the precisions of the impact analysis results that they report. This paper presents the results of this study

Improving Impact Analysis and Regression Testing Using Field Data

Software products are often released with missing functionality, errors, or incompatibilities that may result in failures, inferior performances, or, more generally, user dissatisfaction. In previous work, we presented the Gamma approach, which enables analyses that (1) rely on actual field data instead of synthetic in-house data and (2) leverage the vast and heterogeneous resources of an entire user community

Test-suite augmentation for evolving software

One activity performed by developers during regression testing is test-suite augmentation, which consists of assessing the adequacy of a test suite after a program is modified and identifying new or modified behaviors that are not adequately exercised by the existing test suite and, thus, require additional test cases. In previous work, we proposed MATRIX, a technique for test-suite augmentation based on dependence analysis and partial symbolic execution. In this paper, we present the next step of our work, where we (1) improve the effectiveness of our technique by identifying all relevant change-propagation paths, (2) extend the technique to handle multiple and more complex changes, (3) introduce the first tool that fully implements the technique, and (4) present an empirical evaluation performed on real software. Our results show that our technique is practical and more effective than existing test-suite augmentation approaches in identifying test cases with high fault-detection capabilities