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- 저자 : Muhammad Fermi Pasha (검색결과 3 건)
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A Comprehensive Review on Regression Test Case Prioritization Techniques for Web Services
( Muhammad Hasnain ),( Imran Ghani ),( Muhammad Fermi Pasha ),( Chern Hong Lim ),( Seung Ryul Jeong ) 한국인터넷정보학회 2020 KSII Transactions on Internet and Information Syst Vol.14 No.5
Test Case Prioritization (TCP) involves the rearrangement of test cases on a prioritized basis for various services. This research work focuses on TCP in web services, as it has been a growing challenge for researchers. Web services continuously evolve and hence require reforming and re-execution of test cases to ensure the accurate working of web services. This study aims to investigate gaps, issues, and existing solutions related to test case prioritization. This study examines research publications within popular selected databases. We perform a meticulous screening of research publications and selected 65 papers through which to answer the proposed research questions. The results show that criteria-based test case prioritization techniques are reported mainly in 41 primary studies. Test case prioritization models, frameworks, and related algorithms are also reported in primary studies. In addition, there are eight issues related to TCP techniques. Among these eight issues, optimization and high effectiveness are most discussed within primary studies. This systematic review has identified that a significant proportion of primary studies are not involved in the use of statistical methods in measuring or comparing the effectiveness of TCP techniques. However, a large number of primary studies use ‘Average Percentage of Faults Detected’ (APFD) or extended APFD metrics to compute the performance of techniques for web services.
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Muhammad Hasnain,Imran Ghani,Muhammad Fermi Pasha,Ishrat Hayat Malik,Shahzad Malik 한국인터넷방송통신학회 2019 International Journal of Internet, Broadcasting an Vol.11 No.2
Research classification of software modules was done to validate the approaches proposed for addressing limitations in existing classification approaches. The objective of this study was to replicate the experiments of a recently published research study and re-evaluate its results. The reason to repeat the experiment(s) and re-evaluate the results was to verify the approach to identify the faulty and non-faulty modules applied in the original study for the prioritization of test cases. As a methodology, weconducted this study to re-evaluate the results of the study. The results showed that binary logistic regression analysis remains helpful for researchers for predictions, as it provides an overall prediction of accuracy in percentage. Our study shows a prediction accuracy of 92.9% for the PureMVC Java open source program, while the original study showed an 82% prediction accuracy for the same Java program classes. It is believed by the authors that future research can refine the criteria used to classify classes of web systemswritten in various programming languages based on the results of this study.
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A machine learning framework for performance anomaly detection
Muhammad Hasnain,Muhammad Fermi Pasha,Imran Ghani,정승렬,Aitizaz Ali 한국인터넷정보학회 2022 인터넷정보학회논문지 Vol.23 No.2
Web services show a rapid evolution and integration to meet the increased users' requirements. Thus, web services undergo updates and may have performance degradation due to undetected faults in the updated versions. Due to these faults, many performances and regression anomalies in web services may occur in real-world scenarios. This paper proposed applying the deep learning model and innovative explainable framework to detect performance and regression anomalies in web services. This study indicated that upper bound and lower bound values in performance metrics provide us with the simple means to detect the performance and regression anomalies in updated versions of web services. The explainable deep learning method enabled us to decide the precise use of deep learning to detect performance and anomalies in web services. The evaluation results of the proposed approach showed us the detection of unusual behavior of web service. The proposed approach is efficient and straightforward in detecting regression anomalies in web services compared with the existing approaches.
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