Interactive, visual fault localization support for end-user programmers

End-user programmers are writing an unprecedented number of programs, primarily using languages and environments that incorporate a number of interactive and visual programming techniques. To help these users debug these programs, we have developed an entirely visual, interactive approach to fault localization. This paper presents the approach. We also present the results of a think-aloud study that examined the interactive, human-centric issues that arise in end-user debugging using a fault localization strategy. Our results provide insights into the contributions such strategies can make to the end-user debugging process.Keywords: visual fault localization, debugging, end-user software engineering, slicing, form-based visual programs, testing, end-user programmin

Experimental program analysis

Program analysis techniques are used by software engineers to deduce and infer characteristics of programs for software-engineering-related tasks. Recently, some program analysis techniques have been designed to leverage characteristics of traditional experimentation. An examination of these techniques suggests that a new form of program analysis technique can be created by incorporating characteristics of experimentation. To date, however, there has been little formal recognition by the software engineering community of this form of program analysis, or the implications of using it. This dissertation presents experimental program analysis as a unique paradigm for conducting program analysis. We define this paradigm by building on principles and methodologies underlying the use of experimentation in other fields. This work offers four primary contributions. First, we provide definitions of experimental program analysis, illustrate them by example, and describe several intriguing differences between experimental program analysis and the use of experimentation in other research fields. Second, we survey the research literature for experimental program analysis techniques to offer insights into their existence and diversity—both in terms of the techniques themselves and the problem domains in which they operate. Third, we explore the applicability of experimental program analysis in three software engineering problem domains to provide a formative assessment of the capabilities of these techniques, and how they compare—in terms of cost-effectiveness, utility, and capabilities—to non-experimental baseline techniques. Fourth, we explore the use of experimental program analysis in a large software development setting, and present a case study to investigate the use of experimental program analysis in that setting. The contributions from the foregoing work support the conjecture that the applicability of experimental program analysis could be substantial. In particular, we expect that this work will expose the research community to use of experimental program analysis techniques, identify domains in which experimental program analysis might be useful, suggest many opportunities for improvements to existing techniques, and promote the use of experimentation in program analysis in order to confront program analysis programs in new and innovative ways. Accordingly, we believe that the experimental program analysis paradigm offers a promising new direction for program analysis research

Cost-cognizant test case prioritization for regression testing : an empirical study

Regression testing is a common and necessary task carried out by software practitioners to validate the quality of evolving software systems. Unfortunately, regression testing is often an expensive, time-consuming process, particularly when applied to large software systems. Consequently, practitioners may wish to prioritize the test cases in their regression test suites to execute more importation test cases earlier in the regression testing process. One common goal of this test case prioritization is to increase a test suite’s rate of fault detection, a measure of how quickly faults are detected by a test suite. Recent research has begun to incorporate the practical issues of varying test costs, tests criticalities and fault severities into test case prioritization techniques and metrics measuring the rate of fault detection of these techniques. However, more empirical data is needed to sufficiently examine both the metrics and techniques developed by this research. To address this need, we conducted a case study to further examine the effectiveness of various prioritization techniques that incorporate varying test costs and criticalities. Software practitioners can use the results of this study to help enhance the effectiveness of their regression testing procedures and, in doing so, improve the quality of their software

Predicting Accurate and Actionable Static Analysis Warnings: An Experimental Approach

Static analysis tools report software defects that may or may not be detected by other verification methods. Two challenges complicating the adoption of these tools are spurious false positive warnings and legitimate warnings that are not acted on. This paper reports automated support to help address these challenges using logistic regression models that predict the foregoing types of warnings from signals in the warnings and implicated code. Because examining many potential signaling factors in large software development settings can be expensive, we use a screening methodology to quickly discard factors with low predictive power and cost-effectively build predictive models. Our empirical evaluation indicates that these models can achieve high accuracy in predicting accurate and actionable static analysis warnings, and suggests that the models are competitive with alternative models built without screening