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Statistical Debugging Effectiveness as a Fault Localization Approach: Comparative Study

Journal of Software Engineering and Applications 2016年第8期9卷 412-423页

作者： Ishaq Sandoqa Fawaz Alzghoul Hamad Alsawalqah Isra Alzghoul Loai Alnemer mohammad akour Department of Computer Information Systems The University of Jordan Amman Jordan Department of Computer Information System Yarmouk University Irbid Jordan

Fault localization is an important topic in software testing, as it enables the developer to specify fault location in their code. One of the dynamic fault localization techniques is statistical debugging. In this study, two statistical debugging algorithms are implemented, SOBER and Cause Isolation, and then the experimental works are conducted on five programs coded using Python as an example of well-known dynamic programming language. Results showed that in programs that contain only single bug, the two studied statistical debugging algorithms are very effective to localize a bug. In programs that have more than one bug, SOBER algorithm has limitations related to nested predicates, rarely observed predicates and complement predicates. The Cause Isolation has limitations related to sorting predicates based on importance and detecting bugs in predicate condition. The accuracy of both SOBER and Cause Isolation is affected by the program size. Quality comparison showed that SOBER algorithm requires more code examination than Cause Isolation to discover the bugs.

关键词： Testing and Debugging Dynamic Language Statistical Debugging Fault Localization

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Ensemble-Based Approach for Efficient Intrusion Detection in Network Traffic

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Intelligent Automation & Soft Computing 2023年第8期37卷 2499-2517页

作者： Ammar Almomani Iman akour Ahmed M.Manasrah Omar Almomani mohammad Alauthman Esra’a Abdullah Amaal Al Shwait Razan Al Sharaa School of Computing Skyline University CollegeUniversity City of SharjahP.O.Box 1797SharjahUnited Arab Emirates IT-Department-Al-Huson University College Al-Balqa Applied UniversityP.O.Box 50IrbidJordan Information Systems Department College of Computing&InformaticsUniversity of SharjahUnited Arab Emirates Comp.Info Sciences(CIS)Division Higher Colleges of TechnologySharjahUnited Arab Emirates Computer Sciences Department Yarmouk UniversityIrbidJordan Computer Network and Information Systems Department The World Islamic Sciences and Education UniversityAmman11947Jordan Department of Information Security Faculty of Information TechnologyUniversity of PetraAmmanJordan

The exponential growth of Internet and network usage has neces-sitated heightened security measures to protect against data and network ***,executed through network packets,pose a significant challenge for firewalls to detect and prevent due to the similarity between legit-imate and intrusion *** vast network traffic volume also complicates most network monitoring systems and *** intrusion detection methods have been proposed,with machine learning techniques regarded as promising for dealing with these *** study presents an Intrusion Detection System Based on Stacking Ensemble Learning base(Random For-est,Decision Tree,and k-Nearest-Neighbors).The proposed system employs pre-processing techniques to enhance classification efficiency and integrates seven machine learning *** stacking ensemble technique increases performance by incorporating three base models(Random Forest,Decision Tree,and k-Nearest-Neighbors)and a meta-model represented by the Logistic Regression *** using the UNSW-NB15 dataset,the pro-posed IDS gained an accuracy of 96.16%in the training phase and 97.95%in the testing phase,with precision of 97.78%,and 98.40%for taring and testing,*** obtained results demonstrate improvements in other measurement criteria.

关键词： Intrusion detection system(IDS) machine learning techniques stacking ensemble random forest decision tree k-nearest-neighbor

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