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Leveraging Light-Weight Analyses to Aid Software Maintenance

Fry, Zachary
Thesis/Dissertation; Online
Fry, Zachary
Weimer, Westley
While software systems have become a fundamental part of modern life, they require maintenance to continually function properly and to adapt to potential environment changes. Software maintenance a dominant cost in the software lifecycle, includes both adding new functionality and fixing existing problems, or "bugs," in a system. Software bugs cost the world's economy billions of dollars annually in terms of system down-time and the effort required to fix them. This dissertation focuses specifically on corrective software maintenance --- that is, the process of finding and fixing bugs. Traditionally, managing bugs has been a largely manual process. This historically involved developers treating each defect as a unique maintenance concern, which results in a slow process and thus a high aggregate cost for finding and fixing bugs. Previous work has shown that bugs are often reported more rapidly than companies can address them, in practice. Recently, automated techniques have helped to ease the human burden associated with maintenance activities. However, such techniques often suffer from a few key drawbacks. This thesis argues that automated maintenance tools often target narrowly scoped problems rather than more general ones. Such tools favor maximizing local, narrow success over wider applicability and potentially greater cost benefit. Additionally, this dissertation provides evidence that maintenance tools are traditionally evaluated in terms of functional correctness, while more practical concerns like ease-of-use and perceived relevance of results are often overlooked. When calculating cost savings, some techniques fail to account for the introduction of new workflow tasks while claiming to reduce the overall human burden. The work in this dissertation aims to avoid these weaknesses by providing fully automated, widely-applicable techniques that both reduce the cost of software maintenance and meet relevant human-centric quality and usability standards. This dissertation presents software maintenance techniques that reduce the cost of both finding and fixing bugs, with an emphasis on comprehensive, human-centric evaluation. The work in this thesis uses lightweight analyses to leverage latent information inherent in existing software artifacts. As a result, the associated techniques are both scalable and widely applicable to existing systems. The first of these techniques clusters closely-related, automatically generated defect reports to aid in the process of bug triage and repair. This clustering approach is complimented by an automatic program repair technique that generates and validates candidate defect patches by making sweeping optimizations to a state-of-the-art automatic bug fixing framework. To fully evaluate these techniques, experiments are performed that show net cost savings for both the clustering and program repair approaches while also suggesting that actual human developers both agree with the resulting defect report clusters and also are able to understand and use automatically generated patches. The techniques described in this dissertation are designed to address the three historically-lacking properties noted above: generality, usability, and human-centric efficacy. Notably, both presented approaches apply to many types of defects and systems, suggesting they are generally applicable as part of the maintenance process. With the goal of comprehensive evaluation in mind, this thesis provides evidence that humans both agree with the results of the techniques and could feasibly use them in practice. These and other results show that the techniques are usable, in terms of both minimizing additional human effort via full automation and also providing understandable maintenance solutions that promote continued system quality. By evaluating the associated techniques on programs spanning different languages and domains that contain thousands of bug reports and millions of lines of code, the results presented in this dissertation show potential concrete cost savings with respect to finding and fixing bugs. This work suggests the feasibility of further automation in software maintenance and thus increased reduction of the associated human burdens.
University of Virginia, Department of Computer Science, PHD, 2014
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