닫기
Loading..

전자정보연구정보센터 ICT 융합 전문연구정보의 집대성

영문 논문지

홈 홈 > 연구문헌 > 영문 논문지 > JIPS (한국정보처리학회)

JIPS (한국정보처리학회)

Current Result Document : 2 / 5 이전건 이전건   다음건 다음건

한글제목(Korean Title) Prediction & Assessment of Change Prone Classes Using Statistical & Machine Learning Techniques
영문제목(English Title) Prediction & Assessment of Change Prone Classes Using Statistical & Machine Learning Techniques
저자(Author) Ruchika Malhotra   Ravi Jangra  
원문수록처(Citation) VOL 13 NO. 04 PP. 0778 ~ 0804 (2017. 08)
한글내용
(Korean Abstract)
영문내용
(English Abstract)
Software today has become an inseparable part of our life. In order to achieve the ever demanding needs of customers, it has to rapidly evolve and include a number of changes. In this paper, our aim is to study the relationship of object oriented metrics with change proneness attribute of a class. Prediction models based on this study can help us in identifying change prone classes of a software. We can then focus our efforts on these change prone classes during testing to yield a better quality software. Previously, researchers have used statistical methods for predicting change prone classes. But machine learning methods are rarely used for identification of change prone classes. In our study, we evaluate and compare the performances of ten machine learning methods with the statistical method. This evaluation is based on two open source software systems developed in Java language. We also validated the developed prediction models using other software data set in the same domain (3D modelling). The performance of the predicted models was evaluated using receiver operating characteristic analysis. The results indicate that the machine learning methods are at par with the statistical method for prediction of change prone classes. Another analysis showed that the models constructed for a software can also be used to predict change prone nature of classes of another software in the same domain. This study would help developers in performing effective regression testing at low cost and effort. It will also help the developers to design an effective model that results in less change prone classes, hence better maintenance.
키워드(Keyword) Change Proneness   Empirical Validation   Machine Learning Techniques   Software Quality  
원문 PDF 다운로드