Browsing by Author "Tahir A"

Now showing 1 - 4 of 4
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    An empirical study on the effectiveness of data resampling approaches for cross-project software defect prediction
    (John Wiley and Sons Ltd on behalf of The Institution of Engineering and Technolog, 2022-04) Bennin KE; Tahir A; MacDonell SG; Börstler J
    Cross-project defect prediction (CPDP), where data from different software projects are used to predict defects, has been proposed as a way to provide data for software projects that lack historical data. Evaluations of CPDP models using the Nearest Neighbour (NN) Filter approach have shown promising results in recent studies. A key challenge with defect-prediction datasets is class imbalance, that is, highly skewed datasets where non-buggy modules dominate the buggy modules. In the past, data resampling approaches have been applied to within-projects defect prediction models to help alleviate the negative effects of class imbalance in the datasets. To address the class imbalance issue in CPDP, the authors assess the impact of data resampling approaches on CPDP models after the NN Filter is applied. The impact on prediction performance of five oversampling approaches (MAHAKIL, SMOTE, Borderline-SMOTE, Random Oversampling and ADASYN) and three undersampling approaches (Random Undersampling, Tomek Links and One-sided selection) is investigated and results are compared to approaches without data resampling. The authors examined six defect prediction models on 34 datasets extracted from the PROMISE repository. The authors' results show that there is a significant positive effect of data resampling on CPDP performance, suggesting that software quality teams and researchers should consider applying data resampling approaches for improved recall (pd) and g-measure prediction performance. However, if the goal is to improve precision and reduce false alarm (pf) then data resampling approaches should be avoided.
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    Evil Pickles: DoS attacks based on object-Graph engineering
    (13/05/2017) Dietrich J; Jezek K; Rasheed S; Tahir A; Potanin A
    This artefact demonstrates the effects of the serialisation vulnerabilities described in the companion paper. It is composed of three components: scripts, including source code, for Java, Ruby and C# serialisation-vulnerabilities, two case studies that demonstrate attacks based on the vulnerabilities, and a contracts-based mitigation strategy for serialisation-based attacks on Java applications. The artefact allows users to witness how the serialisation-based vulnerabilities result in behavior that can be used in security attacks. It also supports the repeatability of the case study experiments and the benchmark for the mitigation measures proposed in the paper. Instructions for running the tasks are provided along with a description of the artefact setup.
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    Exploring data management challenges and solutions in agile software development: a literature review and practitioner survey
    (Springer Nature, 2025-05) Fawzy A; Tahir A; Galster M; Liang P; Zimmermann T; Feldt R
    Context: Managing data related to a software product and its development poses significant challenges for software projects and agile development teams. These include integrating data from diverse sources and ensuring data quality amidst continuous change and adaptation. Objective: The paper systematically explores data management challenges and potential solutions in agile projects, aiming to provide insights into data management challenges and solutions for both researchers and practitioners. Method: We employed a mixed-methods approach, including a systematic literature review (SLR) to understand the state-of-research followed by a survey with practitioners to reflect on the state-of-practice. The SLR reviewed 45 studies, identifying and categorizing data management aspects along with their associated challenges and solutions. The practitioner survey captured practical experiences and solutions from 32 industry practitioners who were significantly involved in data management to complement the findings from the SLR. Results: Our findings identified major data management challenges in practice, such as managing data integration processes, capturing diverse data, automating data collection, and meeting real-time analysis requirements. To address these challenges, solutions such as automation tools, decentralized data management practices, and ontology-based approaches have been identified. These solutions enhance data integration, improve data quality, and enable real-time decision-making by providing flexible frameworks tailored to agile project needs. Conclusion: The study pinpointed significant challenges and actionable solutions in data management for agile software development. Our findings provide practical implications for practitioners and researchers, emphasizing the development of effective data management practices and tools to address those challenges and improve project success.
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    Test flakiness’ causes, detection, impact and responses: A multivocal review
    (Elsevier Inc, 2023-12) Tahir A; Rasheed S; Dietrich J; Hashemi N; Zhang L
    Flaky tests (tests with non-deterministic outcomes) pose a major challenge for software testing. They are known to cause significant issues, such as reducing the effectiveness and efficiency of testing and delaying software releases. In recent years, there has been an increased interest in flaky tests, with research focusing on different aspects of flakiness, such as identifying causes, detection methods and mitigation strategies. Test flakiness has also become a key discussion point for practitioners (in blog posts, technical magazines, etc.) as the impact of flaky tests is felt across the industry. This paper presents a multivocal review that investigates how flaky tests, as a topic, have been addressed in both research and practice. Out of 560 articles we reviewed, we identified and analysed a total of 200 articles that are focused on flaky tests (composed of 109 academic and 91 grey literature articles/posts) and structured the body of relevant research and knowledge using four different dimensions: causes, detection, impact and responses. For each of those dimensions, we provide categorization and classify existing research, discussions, methods and tools With this, we provide a comprehensive and current snapshot of existing thinking on test flakiness, covering both academic views and industrial practices, and identify limitations and opportunities for future research.