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Harrold,
Mary Jean
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Mary Jean
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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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ItemA Framework for Understanding Data Dependences(Georgia Institute of Technology, 2002) Orso, Alessandro ; Liang, Donglin ; Sinha, Saurabh ; Harrold, Mary JeanIdentifying and understanding data dependences is important for a variety of software-engineering tasks. The presence of pointers, arrays, and dynamic memory allocation introduces subtle and complex data dependences that may be difficult to understand. In this paper, we present a refinement of our previously developed classification that also distinguishes the types of memory locations, considers interprocedural data dependences, and further distinguishes such data dependences based on the kinds of interprocedura paths on which they occur. This new classification enables reasoning about the complexity of data dependences in programs using features such as pointers, arrays, and dynamic memory allocation. We present an algorithm for computing interprocedural data dependences according to our classification. To evaluate the classification, we compute the distribution of data dependences for a set of real C programs and we discuss how the distribution can be useful in understanding the characteristics of a program. We also evaluate how alias information provided by different algorithms, varying in precision, affects the distribution. Finally, we investigate how the classification can be exploited to estimate complexity of the data dependences in a program.
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ItemIncremental Slicing Based on Data-Dependences Types(Georgia Institute of Technology, 2000) Orso, Alessandro ; Sinha, Saurabh ; Harrold, Mary JeanProgram slicing is useful for assisting with software-maintenance tasks, such as program understanding, debugging, impact analysis, and regression testing. The presence and frequent usage of pointers, in languages such as C, causes complex data dependences. To function effectively on such programs, slicing techniques must account for pointerinduced data dependences. Although many existing slicing techniques function in the presence of pointers, none of those techniques distinguishes data dependences based on their types. This paper presents a new slicing technique, in which slices are computed based on types of data dependences. This new slicing technique offers several benefits and can be exploited in different ways, such as identifying subtle data dependences for debugging purposes, computing reduced-size slices quickly for complex programs, and performing incremental slicing. In particular, this paper describes an algorithm for incremental slicing that increases the scope of a slice in steps, by incorporating different types of data dependences at each step. The paper also presents empirical results to illustrate the performance of the technique in practice. The experimental results show how the sizes of the slices grow for different small- and mediumsized subjects. Finally, the paper presents a case study that explores a possible application of the slicing technique for debugging.
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ItemEffects of Pointers on Data Dependences(Georgia Institute of Technology, 2000) Orso, Alessandro ; Sinha, Saurabh ; Harrold, Mary JeanData dependences, which relate statements that compute data value to statements that use those values, are useful for automating a variety of program-comprehension-related activities, such as reverse engineering, impact analysis, and debugging. Unfortunately, data dependences are difficult to compute and understand in the presence of commonly-used language features such as pointers, arrays, and structures. To facilitate the comprehension of data dependences in programs that use such features, we define a technique for computing and classifying data dependences that takes into account the complexities introduced by specific language constructs. The classification that we present is finer-grained than previously proposed classification. Moreover, unlike previous work, we present empirical results that illustrate the distribution of data dependences for a set of C subjects. We also present a potential application for the proposed classification: program slicing. We propose a technique that allows for computing slices based on data-dependence types. This technique facilitates the use of slicing for understanding a program because a user can either incrementally augment a slice by incorporating data dependences based on their relevance, or focus on specific kinds of dependences. Finally, we present a case study that shows how the incremental computation of slices can (1) highlight subtle data dependences within a program, and (2) provide useful information about those dependences.
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ItemAnalysis and Testing of Programs with Exception-Handling Constructs(Georgia Institute of Technology, 2000) Sinha, Saurabh ; Harrold, Mary JeanAnalysis techniques, such as control flow, data flow, and control dependence, are used for a variety of software-engineering tasks, including structural and regression testing, dynamic execution profiling, static and dynamic slicing, and program understanding. To be applicable to programs in languages such as Java and C++, these analysis techniques must account for the effects of exception occurrences and exception-handling constructs; failure to do so can cause the analysis techniques to compute incorrect results and thus, limit the usefulness of the applications that use them. This paper discusses the effect of exception-handling constructs on several analysis techniques. The paper presents techniques to construct representations for programs with explicit exception occurrences --- exceptions that are raised explicitly through throw statements --- and exception-handling constructs. The paper presents algorithms that use these representations to perform the desired analyses. The paper also discusses several software-engineering applications that use these analyses. Finally, the paper describes empirical results pertaining to the occurrence of exception-handling constructs in Java programs, and their impact on some analysis tasks.