UMD Theses and Dissertations

Permanent URI for this collectionhttp://hdl.handle.net/1903/3

New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.

More information is available at Theses and Dissertations at University of Maryland Libraries.

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Now showing 1 - 5 of 5
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    Model-Based Testing of Off-Nominal Behaviors
    (2017) Schulze, Christoph; Cleaveland, Rance; Lindvall, Mikael; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Off-nominal behaviors (ONBs) are unexpected or unintended behaviors that may be exhibited by a system. They can be caused by implementation and documentation errors and are often triggered by unanticipated external stimuli, such as unforeseen sequences of events, out of range data values, or environmental issues. System specifications typically focus on nominal behaviors (NBs), and do not refer to ONBs or their causes or explain how the system should respond to them. In addition, untested occurrences of ONBs can compromise the safety and reliability of a system. This can be very dangerous in mission- and safety-critical systems, like spacecraft, where software issues can lead to expensive mission failures, injuries, or even loss of life. In order to ensure the safety of the system, potential causes for ONBs need to be identified and their handling in the implementation has to be verified and documented. This thesis describes the development and evaluation of model-based techniques for the identification and documentation of ONBs. Model-Based Testing (MBT) techniques have been used to provide automated support for thorough evaluation of software behavior. In MBT, models are used to describe the system under test (SUT) and to derive test cases for that SUT. The thesis is divided into two parts. The first part develops and evaluates an approach for the automated generation of MBT models and their associated test infrastructure. The test infrastructure is responsible for executing the generated test cases of the models. The models and the test infrastructure are generated from manual test cases for web-based systems, using a set of heuristic transformation rules and leveraging the structured nature of the SUT. This improvement to the MBT process was motivated by three case studies of MBT that we conducted that evaluate MBT in terms of its effectiveness and efficiency for identifying ONBs. Our experience led us to develop automated approaches to model and test-infrastructure creation, since these were some of the most time-consuming tasks associated with MBT. The second part of the thesis presents a framework and associated tooling for the extraction and analysis of specifications for identifying and documenting ONBs. The framework infers behavioral specifications in the form of system invariants from automatically generated test data using data-mining techniques (e.g. association-rule mining). The framework follows an iterative test -> infer -> instrument -> retest paradigm, where the initial invariants are refined with additional test data. This work shows how the scalability and accuracy of the resulting invariants can be improved with the help of static data- and control-flow analysis. Other improvements include an algorithm that leverages the iterative process to accurately infer invariants from variables with continuous values. Our evaluations of the framework have shown the utility of such automatically generated invariants as a means for updating and completing system specifications; they also are useful as a means of understanding system behavior including ONBs.
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    A Binary Classifier for Test Case Feasibility Applied to Automatically Generated Tests of Event-Driven Software
    (2016) Robbins, Bryan Thomas; Memon, Atif; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Modern software application testing, such as the testing of software driven by graphical user interfaces (GUIs) or leveraging event-driven architectures in general, requires paying careful attention to context. Model-based testing (MBT) approaches first acquire a model of an application, then use the model to construct test cases covering relevant contexts. A major shortcoming of state-of-the-art automated model-based testing is that many test cases proposed by the model are not actually executable. These \textit{infeasible} test cases threaten the integrity of the entire model-based suite, and any coverage of contexts the suite aims to provide. In this research, I develop and evaluate a novel approach for classifying the feasibility of test cases. I identify a set of pertinent features for the classifier, and develop novel methods for extracting these features from the outputs of MBT tools. I use a supervised logistic regression approach to obtain a model of test case feasibility from a randomly selected training suite of test cases. I evaluate this approach with a set of experiments. The outcomes of this investigation are as follows: I confirm that infeasibility is prevalent in MBT, even for test suites designed to cover a relatively small number of unique contexts. I confirm that the frequency of infeasibility varies widely across applications. I develop and train a binary classifier for feasibility with average overall error, false positive, and false negative rates under 5\%. I find that unique event IDs are key features of the feasibility classifier, while model-specific event types are not. I construct three types of features from the event IDs associated with test cases, and evaluate the relative effectiveness of each within the classifier. To support this study, I also develop a number of tools and infrastructure components for scalable execution of automated jobs, which use state-of-the-art container and continuous integration technologies to enable parallel test execution and the persistence of all experimental artifacts.
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    COLLABORATIVE TESTING ACROSS SHARED SOFTWARE COMPONENTS
    (2015) Long, Teng; Sussman, Alan; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Large component-based systems are often built from many of the same components. As individual component-based software systems are developed, tested and maintained, these shared components are repeatedly manipulated. As a result there are often significant overlaps and synergies across and among the different test efforts of different component-based systems. However, in practice, testers of different systems rarely collaborate, taking a test-all-by-yourself approach. As a result, redundant effort is spent testing common components, and important information that could be used to improve testing quality is lost. The goal of this research is to demonstrate that, if done properly, testers of shared software components can save effort by avoiding redundant work, and can improve the test effectiveness for each component as well as for each component-based software system by using information obtained when testing across multiple components. To achieve this goal I have developed collaborative testing techniques and tools for developers and testers of component-based systems with shared components, applied the techniques to subject systems, and evaluated the cost and effectiveness of applying the techniques. The dissertation research is organized in three parts. First, I investigated current testing practices for component-based software systems to find the testing overlap and synergy we conjectured exists. Second, I designed and implemented infrastructure and related tools to facilitate communication and data sharing between testers. Third, I designed two testing processes to implement different collaborative testing algorithms and applied them to large actively developed software systems. This dissertation has shown the benefits of collaborative testing across component developers who share their components. With collaborative testing, researchers can design algorithms and tools to support collaboration processes, achieve better efficiency in testing configurations, and discover inter-component compatibility faults within a minimal time window after they are introduced.
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    Understanding, Discovering and Leveraging a Software System's Effective Configuration Space
    (2011) Song, Charles; Porter, Adam; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Many modern software systems are highly configurable. While a high degree of configurability has many benefits, such as extensibility, reusability and portability, it also has its costs. In the worst case, the full configuration space of a system is the exponentially large combination of all possible option settings and every configuration can potentially produce unique behavior in the software system. Therefore, this software configuration space explosion problem adds combinatorial complexity to many already difficult software engineering tasks. To date, much of the research in this area has tackled this problem using black-box techniques, such as combinatorial interaction testing (CIT). Although these techniques are promising in systematizing the testing and analysis of configurable systems, they ignore a system's internal structure and we think that is a huge missed opportunity. We hypothesize that systems are often structured such that their effective configuration spaces -- the set of configurations needed to achieve a specific goal -- are often much smaller than their full configuration spaces. And if we can efficiently identify or approximate the effective configuration spaces, then we can use that information to greatly improve various software engineering tasks. To understand the effective configuration spaces of software systems, we used symbolic evaluation, a white-box analysis, to capture all executions a system can take under any configuration. The symbolic evaluation results confirmed that the effective configuration spaces are in fact the composition of many small, self-contained groupings of options. And we developed analysis techniques to succinctly characterize how configurations interact with a system's internal structures. We showed that while the majority of a system's interactions are relatively low strength, some important high-strength interactions do exist, and that existing approaches such as CIT are highly unlikely to generate them in practice. Results from our in-depth investigations serve as the foundation for developing new approaches to efficiently discovering effective configuration spaces. We proposed a new algorithm called interaction tree discovery (iTree) that aims to identify sets of configurations that are smaller than those generated by CIT, while also including important high-strength interactions missed by practical applications of CIT. On each iteration of iTree, we first use low-strength covering array to test the system under, and then apply machine learning techniques to discover new interactions that are potentially responsible for any new coverage seen. By repeating this process, iTree builds up a set of configurations likely to contain key high-strength interactions. We evaluated iTree and our results strongly suggest that iTree can identify high-coverage sets of configurations more effectively than traditional CIT or random sampling. We next developed the interaction learning approach that estimates the configuration interactions underlying the effective configuration space by building classification models for iTree execution results. This approach is light-weight, yet produces accurate estimates of the interactions; making leveraging effective configuration spaces practical for many software engineering tasks. Using this approach, we were able to approximate the effective configuration space of the ~1M-LOC MySQL, something that is infeasible using existing techniques, at very low cost.
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    A Framework for Detecting and Diagnosing Configuration Faults in Web Applications
    (2007-11-26) Eaton, Cyntrica Neshe; Memon, Atif; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Software portability is a key concern when target operational environments are highly configurable; variations in configuration settings can significantly impact software correctness. While portability is key for a wide range of software types, it is a significant challenge in web application development. The client configuration used to navigate and interact with web content is known to be an important factor in the subsequent quality of deployed web applications. With the widespread use of diverse, heterogeneous web client configurations, the results of web application deployment can vary unpredictably among users. Given existing approaches and limited development resources, attempting to develop web applications that are viewable, functional, and portable for the vast web configuration space is a significant undertaking. As a result, faults that only surface in precise configurations, termed configuration faults, have the potential to escape detection until web applications are fielded. This dissertation presents an automated, model-based framework that uses static analysis to detect and diagnose web configuration faults. This approach overcomes the limitations of current techniques by featuring an extensible model of the configuration space that enables efficient portability analysis across the vast array of client environments. The basic idea behind this approach is that source code fragments (i.e., HTML tags and CSS rules) embedded in web application source code adversely impact portability of web applications when they are unsupported in target client configurations; without proper support, the source code is either processed incorrectly or ignored, resulting in configuration faults. Using static analysis, configuration fault detection is performed by applying a model of the web application source against knowledge of support criteria; any unsupported source code detected is considered an index to potential configuration faults. In the effort to fully exploit this approach, improve practicality, and maximize fault detection efficiency, manual and automated approaches to knowledge acquisition have been implemented, variations of web application and client support knowledge models have been investigated, and visualization of configuration fault detection results has been explored. To optimize the automated acquisition of support knowledge, alternate learning strategies have been empirically investigated and provisions for capturing tag interaction have been integrated into the process.