Saudi Cultural Missions Theses & Dissertations
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Item Restricted Interval Analysis and Methods in Software Analysis(Manchester University, 2024) Aldughaim, Mohannad; Cordeiro, LucasThis thesis investigates the application of interval analysis and methods within the domain of software verification, with a particular focus on mitigating the state space explosion problem. State space explosion poses a significant challenge to static and dynamic software verification techniques, such as fuzzing, bounded model checking (BMC), and abstract interpretation. These methods, despite their robustness, struggle to scale when faced with complex programs that generate a vast number of execution paths and states. To address this, the thesis introduces the use of contractors—interval methods that refine the search space by eliminating non-solution regions—across several verification frameworks. By applying interval contractors in fuzzing, BMC, and abstract interpretation, the search space is systematically reduced without compromising the soundness or completeness of the verification process. Contractors are employed to navigate guard conditions, narrow down variable domains, and simplify control structures, leading to a more efficient exploration of execution paths. The thesis presents a detailed implementation of these methods and evaluates their performance through rigorous benchmarking. Results demonstrate that the integration of contractors significantly enhances verification efficiency, reducing both computational resource consumption and time while preserving accuracy in identifying potential software vulnerabilities. This research offers a novel contribution to improving the scalability of software verification methods, making them more practical for real-world applications.27 0Item Restricted Dynamic Feature Location Framework for Software Project(University of Bahrain, 2024-08) Buzaid, Faisal; Albalooshi, FawziThe Dynamic Feature Location Techniques (DFLTs) aim to automate the process of identifying the source code responsible for executing specific features within software systems. Manual implementation of DFLTs is time-consuming and demanding for developers, leading to the proposal of semi-automated approaches. One common approach involves generating execution traces by executing multiple scenarios for each software feature and then mapping the corresponding source code based on these traces. However, the execution traces are often large and contain irrelevant data to the software feature, requiring solutions to reduce their size and the eliminate irrelevant data. One such solution involves minimizing the number of scenarios needed to exercise a software feature, but little work has been done in this area. To address this gap, a generic framework called Aggregation of Execution Traces to Formulate a Scenario (AETFS) is introduced in this work. AETFS leverages runtime software output and employs textual analysis techniques to extract relevant data from the execution trace for scenario creation. It explores textual analysis, including topic modeling, as a means to select accurate scenarios for DFLTs. The performance of AETFS is characterized in terms of execution trace granularity, enabling the identification of meaningful terms that can filter the execution trace using textual analysis techniques such as Latent Semantic Indexing (LSI). The evaluation encompasses eight subject systems with 600 features, making it more extensive than previous studies. The study identifies certain attributes of execution traces and text queries that impact AETFS’s performance. Two distinct groups emerge, one achieving superior Feature Location (FL) using AETFS and the other achieving better FL using a conventional baseline method. Combining AETFS with the baseline method significantly enhances performance, with the top results surpassing the baseline by 45% and the lowest by 12% over AETFS. In conclusion, this work highlights the importance of rigorously characterizing the proposed DFLTs framework to identify optimal scenarios for exercising software features. It emphasizes the need to differentiate between scenarios and their characterizations to generate necessary insights. The findings demonstrate the effectiveness of AETFS while providing valuable insights for further advancements in the field of DFLTs.14 0