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Harrold,
Mary Jean
Harrold,
Mary Jean
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ItemSPA: Symbolic Program Approximation for Scalable Path-sensitive Analysis(Georgia Institute of Technology, 2009) Harrold, Mary Jean ; Santelices, RaulSymbolic 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.
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ItemImproving the Classification of Software Behaviors using Ensembles(Georgia Institute of Technology, 2005) Bowring, James Frederick ; Harrold, Mary Jean ; Rehg, James M.One approach to the automatic classification of program behaviors is to view these behaviors as the collection of all the program's executions. Many features of these executions, such as branch profiles, can be measured, and if these features accurately predict behavior, we can build automatic behavior classifiers from them using statistical machine-learning techniques. Two key problems in the development of useful classifiers are (1) the costs of collecting and modeling data and (2) the adaptation of classifiers to new or unknown behaviors. We address the first problem by concentrating on the properties and costs of individual features and the second problem by using the active-learning paradigm. In this paper, we present our technique for modeling a data-flow feature as a stochastic process exhibiting the Markov property. We introduce the novel concept of databins to summarize, as Markov models, the transitions of values for selected variables. We show by empirical studies that databin-based classifiers are effective. We also describe ensembles of classifiers and how they can leverage their components to improve classification rates. We show by empirical studies that ensembles of control-flow and data-flow based classifiers can be more effective than either component classifier.
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ItemUnderstanding Data Dependences in the Presence of Pointers(Georgia Institute of Technology, 2003) Orso, Alessandro ; Sinha, Saurabh ; Harrold, Mary JeanUnderstanding data dependences in programs is important for many software-engineering activities, such as program understanding, impact analysis, reverse engineering, and debugging. The presence of pointers, arrays, and structures can cause subtle and complex data dependences that can be difficult to understand. For example, in languages such as C, an assignment made through a pointer dereference can assign a value to one of several variables, none of which may appear syntactically in that statement. In the first part of this paper, we describe two techniques for classifying data dependences in the presence of pointer dereferences. The first technique classifies data dependences based on definition type, use type, and path type. The second technique classifies data dependences based on span. We present empirical results to illustrate the distribution of data-dependence types and spans for a set of real C programs. In the second part of the paper, we discuss two applications of the classification techniques. First, we investigate different ways in which the classification can be used to facilitate data-flow testing and verification. We outline an approach that uses types and spans of data dependences to determine the appropriate verification technique for different data dependences; we present empirical results to illustrate the approach. Second, we present a new slicing paradigm that computes slices based on types of data dependences. Based on the new paradigm, we define an incremental slicing technique that computes a slice in multiple steps. We present empirical results to illustrate the sizes of incremental slices and the potential usefulness of incremental slicing for debugging.
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ItemSoftware Behavior: Automatic Classification and its Applications(Georgia Institute of Technology, 2003) Bowring, James Frederick ; Rehg, James M. ; Harrold, Mary JeanA program's behavior is ultimately the collection of all its executions. This collection is diverse, unpredictable, and generally unbounded. Thus it is especially suited to statistical analysis and machine learning techniques. We explore the thesis that 1st- and 2nd-order Markov models of event-transitions are effective predictors of program behavior. We present a technique that models program executions as Markov models, and a clustering method for Markov models that aggregates multiple program executions, yielding a statistical description of program behaviors. With this approach, we can train classifiers to recognize specific behaviors emitted by an execution without knowledge of inputs or outcomes. We evaluate an application of active learning to the efficient refinement of our classifiers by conducting three empirical studies that explore a scenario illustrating automated test plan augmentation. We present a set of potential research questions and applications that our work suggests.