Person:

Harrold, Mary Jean

Permanent Link

https://hdl.handle.net/1853/71329

Associated Organization(s)

Organizational Unit

School of Computer Science

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Publication Search Results

Now showing 1 - 10 of 12

Visualization of Exception Handling Constructs to Support Program Understanding

(Georgia Institute of Technology, 2009) Shah, Hina ; Görg, Carsten ; Harrold, Mary Jean

This paper presents a new visualization technique for supporting the understanding of exception-handling constructs in Java programs. To understand the requirements for such a visualization, we surveyed a group of software developers, and used the results of that survey to guide the creation of the visualizations. The technique presents the exception-handling information using three views: the quantitative view, the flow view, and the contextual view. The quantitative view provides a high-level view that shows the throw-catch interactions in the program, along with relative numbers of these interactions, at the package level, the class level, and the method level. The flow view shows the type-throw-catch interactions, illustrating information such as which exception types reach particular throw statements, which catch statements handle particular throw statements, and which throw statements are not caught in the program. The contextual view shows, for particular type-throw-catch interactions, the packages, classes, and methods that contribute to that exception-handling construct. We implemented our technique in an Eclipse plugin called EnHanCe and conducted a usability and utility study with participants in industry.
HDCCSR: software self-awareness using dynamic analysis and Markov models

(Georgia Institute of Technology, 2008-12-20) Harrold, Mary Jean ; Rugaber, Spencer ; Rehg, James M.
A Framework for Understanding Data Dependences

(Georgia Institute of Technology, 2002) Orso, Alessandro ; Liang, Donglin ; Sinha, Saurabh ; Harrold, Mary Jean

Identifying 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.
Gamma System: Continuous Evolution of Software after Deployment

(Georgia Institute of Technology, 2002) Orso, Alessandro ; Liang, Donglin ; Harrold, Mary Jean ; Lipton, Richard J.

In this paper, we present the Gamma system---a new approach for continuous improvement of software systems after their deployment. The Gamma system facilitates remote monitoring of deployed software using a revolutionary approach that exploits the opportunities presented by a software product being used by many users connected through a network. Gamma splits monitoring tasks across different instances of the software, so that partial information can be collected from different users by means of light-weight instrumentation, and integrated to gather the overall monitoring information. This system enables software producers (1) to perform continuous, minimally intrusive analyses of their software's behavior, and (2) to use the information thus gathered to improve and evolve their software. We describe the Gamma system and its underlying technology in detail, and illustrate the different components of the system. We also present a prototype implementation of the system and show our initial experiences with it.
A Technique for Dynamic Updating of Java Software

(Georgia Institute of Technology, 2002) Orso, Alessandro ; Rao, Anup ; Harrold, Mary Jean

TDuring maintenance, systems are updated to correct faults, improve functionality, and adapt the software to changes in its execution environment. The typical software-update process consists of stopping the system to be updated, performing the actual update of the code, and restarting the system. For systems such as banking and telecommunication software, however, the cost of downtime can be prohibitive. The situation is even worse for systems such as air-traffic controllers and life-support software, for which a shut-down is in general not an option. In those cases, the use of some form of on-the-fly program modification is required. In this paper, we propose a new technique for dynamic updating of Java software. Our technique is based on the use of proxy classes and does not require any support from the runtime system. The technique allows for updating a running Java program by substituting, adding, and deleting classes. We also present Dusc (Dynamic Updating through Swapping of Classes), a tool that we developed and that implements our technique. Finally, we describe an empirical study that we performed to validate the technique on a real Java subject. The results of the study show that our technique can be effectively applied to Java software with only little overhead in both execution time and program size.
Paper: Evaluating the Precision of Static Reference Analysis Using Profiling

(Georgia Institute of Technology, 2002) Liang, Donglin ; Pennings, Maikel ; Harrold, Mary Jean

Program analyses and optimization of Java programs require reference information that determines the instances that may be accessed through dereferences. Reference information can be computed using reference analysis. This paper presents a set of studies that evaluate the precision of some existing approaches for identifying instances and for computing reference information in a reference analysis. The studies use dynamic reference information collected during run-time as a lower bound approximation to the precise reference information. The studies measure the precision of an existing approach by comparing the information computed using the approach with the lower bound approximation. The paper also presents case studies that attempt to identify the cases under which an existing approach is not effective. The presented studies provide information that may guide the usage of existing reference analysis techniques and the development of new reference analysis techniques.
Extending and Evaluating Flow-Insensitve and Context-insensitive Points-to Analyses for Java

(Georgia Institute of Technology, 2001) Liang, Donglin ; Pennings, Maikel ; Harrold, Mary Jean

This paper presents extensions to Steensgaard's and Andersen's algorithms to handle Java features. Without careful consideration, the handling of these features may affect the correctness, precision, and efficiency of these algorithms. The paper also presents the results of empirical studies. These studies compare the precision and efficiency of these two algorithms and evaluate the effectiveness of handling Java features using alternative approaches. The studies also evaluate the impact of the points-to information provided by these two algorithms on client analyses that use the information.
Using Component Metadata to Support the Regression Testing of Component-Based Software

(Georgia Institute of Technology, 2000) Harrold, Mary Jean ; Orso, Alessandro ; Rosenblum, David S. ; Rothermel, Gregg ; Soffa, Mary Lou ; Do, Hyunsook

Interest in component-based software continues to grow with the recognition of its potential in managing the increasing complexity of software systems. However, the use of externally-provided components has serious drawbacks, in most cases due to the lack of information about the components, for a wide range of activities in the engineering of component-based applications. Consider the activity of regression testing, whose high cost has been, and continues to be, a problem. In the case of component-based applications, regression testing can be even more expensive. When a new version of one or more components is integrated into an application, the lack of information about such externally-developed components makes it difficult to effectively determine the test cases that should be rerun on the resulting application. In previous work, we proposed the use of metadata, which are additional data provided with a component, to support software engineering tasks. In this paper, we present two new metadata-based techniques that address the problem of regression test selection for component-based applications: a code-based approach and a specification-based approach. First, using an example, we illustrate the two techniques. Then, we present a case study that applies the code-based technique to a real component-based system. The results of the study indicate that, on average, 26% of the overall testing effort can be saved over seven releases of the component-based system studied, with a maximum savings of 99% of the testing effort for one version. This reduction demonstrates that metadata can produce benefits in regression testing by reducing the costs related to this activity.
Incremental Slicing Based on Data-Dependences Types

(Georgia Institute of Technology, 2000) Orso, Alessandro ; Sinha, Saurabh ; Harrold, Mary Jean

Program 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.
Effects of Pointers on Data Dependences

(Georgia Institute of Technology, 2000) Orso, Alessandro ; Sinha, Saurabh ; Harrold, Mary Jean

Data 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.