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Harrold, Mary Jean

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    Probabilistic Slicing for Predictive Impact Analysis
    (Georgia Institute of Technology, 2010) Santelices, Raul ; Harrold, Mary Jean
    Program slicing is a technique that determines which statements in a program affect or are affected by another statement in that program. Static forward slicing, in particular, can be used for impact analysis by identifying all potential effects of changes in software. This information helps developers design and test their changes. Unfortunately, static slicing is too imprecise—it often produces large sets of potentially affected statements, limiting its usefulness. To reduce the resulting set of statements, other forms of slicing have been proposed, such as dynamic slicing and thin slicing, but they can miss relevant statements. In this paper, we present a new technique, called Probabilistic Slicing (p-slicing), that augments a static forward slice with a relevance score for each statement by exploiting the observation that not all statements have the same probability of being affected by a change. P-slicing can be used, for example, to focus the attention of developers on the “most impacted” parts of the program first. It can also help testers, for example, by estimating the difficulty of “killing” a particular mutant in mutation testing and prioritizing test cases. We also present an empirical study that shows the effectiveness of p-slicing for predictive impact analysis and we discuss potential benefits for other tasks.
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    Applying Aggressive Propagation-based Strategies for Testing Changes
    (Georgia Institute of Technology, 2010) Santelices, Raul ; Harrold, Mary Jean
    Test-suite augmentation for evolving software— the process of augmenting a test suite to adequately test software changes—is necessary for any program that undergoes modifications as part of its development and maintenance cycles. Recently, we presented a new technique for test-suite augmentation based on leveraging the propagation conditions for the effects of changes. Although empirical studies show that this technique can be quite effective for testing changes, the experiments have been limited because of the complexity of the implementation. In this paper, we present a new and more efficient approach for propagation-based testing of changes that can reach much longer propagation-distances and can focus the testing more precisely on those behaviors of changes that can actually affect the output. Using an implementation of this new approach, we performed a study on a set of changes on Java programs for which we compared, to a much larger extent, our propagation-based strategy with other existing techniques for testing changes. The results of the study not only confirm the superior effectiveness of propagation-based strategies over these other techniques for testing changes, but also quantify that superiority and clarify the conditions under which our approach is most effective.
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    SPA: Symbolic Program Approximation for Scalable Path-sensitive Analysis
    (Georgia Institute of Technology, 2009) Harrold, Mary Jean ; Santelices, Raul
    Symbolic execution is a static-analysis technique that has been used for applications such as test-input generation and change analysis. Symbolic execution’s path sensitivity makes scaling it difficult. Despite recent advances that reduce the number of paths to explore, the scalability problem remains. Moreover, there are applications that require the analysis of all paths in a program fragment, which exacerbate the scalability problem. In this paper, we present a new technique, called Symbolic Program Approximation (SPA), that performs an approximation of the symbolic execution of all paths between two program points by abstracting away certain symbolic subterms to make the symbolic analysis practical, at the cost of some precision. We discuss several applications of SPA, including testing of software changes and static invariant discovery. We also present a tool that implements SPA and an empirical evaluation on change analysis and testing that shows the applicability, effectiveness, and potential of our technique.