earticle

논문검색

Investigating the Regression Analysis Results for Classification in Test Case Prioritization : A Replicated Study

초록

영어

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, we conducted 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 systems written in various programming languages based on the results of this study.

목차

Abstract
1. Introduction
2. Background to the original study
2.1 This Replication
3. Related work
3.1 Clustering and Classification Algorithms
4. Experimental set up
4.3 Comparison with the Results of the Original Study
5. Threats to Validity
6. Conclusion and Future Work
References

저자정보

  • Muhammad Hasnain School of IT, Monash University Malaysia
  • Imran Ghani Department of Computer Science, Indiana University of Pennsylvania, USA
  • Muhammad Fermi Pasha School of IT, Monash University Malaysia
  • Ishrat Hayat Malik School of IT, Monash University Malaysia
  • Shahzad Malik NUST University Islamabad Pakistan

참고문헌

자료제공 : 네이버학술정보

    함께 이용한 논문

      ※ 기관로그인 시 무료 이용이 가능합니다.

      • 4,000원

      0개의 논문이 장바구니에 담겼습니다.