An Assessment of Eclipse Bugs' Priority and Severity Prediction Using Machine Learning

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Mohammed Q. Shatnawi
Batool Alazzam


The reliability and quality of software programs remains to be an important and challenging aspect of software design. Software developers and system operators spend huge time on assessing and overcoming expected and unexpected errors that might affect the users’ experience negatively. One of the major concerns in developing software problems is the bug reports, which contains the severity and priority of these defects. For a long time, this task was performed manually with huge effort and time consumptions by system operators. Therefore, in this paper, we present a novel automatic assessment tool using Machine Learning algorithms, for assessing bugs’ reports based on several features such as hardware, product, assignee, OS, component, target milestone, votes, and versions.  The aim is to build a tool that automatically classifies software bugs according to the severity and priority of the bugs and makes predictions based on the most representative features and bug report text. To perform this task, we used the Multi-Nominal Naive Bayes, Random Forests Classifier, Bagging, Ada Boosting, SVC, KNN, and Linear SVM Classifiers and Natural Language Processing techniques to analyze the Eclipse dataset. The approach shows promising results for software bugs’ detection and prediction.

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How to Cite
Shatnawi, M. Q., & Alazzam, B. (2022). An Assessment of Eclipse Bugs’ Priority and Severity Prediction Using Machine Learning. International Journal of Communication Networks and Information Security (IJCNIS), 14(1). (Original work published April 12, 2022)
Research Articles
Author Biography

Mohammed Q. Shatnawi, Computer Information Systems Department Faculty of Computer and Information Technology Jordan University of Science and Technology

A results-driven professional with experience in business solutions’ assessment and development for many years. Through my years of experience, I have managed to bridge the gap between sustainable business solutions and the company’s vision. I have excellent problem solving techniques and recommendations for current business needs with flexibility to adapt emerging updates. I am now a business consultant in Asinat Training Academy, and responsible for IT solutions’ development, integration and communication.With my constant race of keeping up with the latest trends in the field, I have enrolled in many workshops and training in Data Science and Big Data Management. I seek expert level knowledge in the big data analytical tools and methodologies. I mastered in depth practical expertise in Hadoop Ecosystem tools such as HDFS, Spark, Pig, Hive and HBase. I am now the instructor and coordinator of the "Big Data Management" course for masters' and bachelors' students in Jordan University of Science and Technology and is currently working on multiple research projects in the field. I was also an adviser for the development of the academic curriculum for the Data Science masters' degree in JUST and maintaining the objectives and outcomes of courses with the reference to the courses' description. Moreover, I have held several administrative positions in JUST. For example, vice director of the computer and information center, vice dean for the IT deanship, department’s chair and assistant’s dean for the graduate school deanship. Eventually, I have earned the PMP certificate in March 2018.


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