Document Type : Research Article
Authors
1 Shiraz university of technology , Shiraz , Iran
2 Shiraz university of Technology Shiraz Iran
Abstract
Keywords
[1] | B. Li, X. Sun, H. Leung, and S. Zhang. A survey of code-based change impact analysis techniques. Software Testing, Verification and Reliability, 23(8):613--646, 2013. [ bib | DOI ] |
[2] | A. Marcus, A. Sergeyev, V. Rajlich, and J.I. Maletic. An Information Retrieval Approach to Concept Location in Source Code. In 11th Working Conference on Reverse Engineering, pages 214--223. IEEE, 2004. [ bib | DOI ] |
[3] | G. Canfora and L. Cerulo. Impact Analysis by Mining Software and Change Request Repositories. In 11th IEEE International Software Metrics Symposium (METRICS'05), page 29. IEEE, 2005. [ bib | DOI ] |
[4] | M. Torchiano and F. Ricca. Impact analysis by means of unstructured knowledge in the context of bug repositories. In Proceedings of the 2010 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement, page 47. ACM, 2010. [ bib | DOI ] |
[5] | C. D. Manning, P. Raghavan, and H. Schütze. Introduction to information retrieval. Cambridge University Press, 2008. [ bib ] |
[6] | S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, and R. Harshman. Indexing by Latent semantic analysis. Journal of the American society for information science, 41(6):391--407, 1990. [ bib | DOI ] |
[7] | C. Gupta, S. Yogesh, and D. S. Chauhan. Dependency based process model for impact analysis: a requirement engineering perspective. International journal of computer applications, 6(6):28--30, 2010. [ bib | DOI ] |
[8] | D. M.German, A. E.Hassan, and G. Robles. Change Impact Graphs: Determining the Impact of Prior Code Changes. Information and Software Technology, 51(10):1394--1408, 2009. [ bib | DOI ] |
[9] | E. Ufuktepe and T. Tuglular. A Program Slicing-Based Bayesian Network Model for Change Impact Analysis. In 2018 IEEE International Conference on Software Quality, Reliability and Security (QRS), pages 490--499. IEEE, 2018. [ bib | DOI ] |
[10] | C. Carrillo and R. Capilla. Ripple effect to evaluate the impact of changes in architectural design decisions. In Proceedings of the 12th European Conference on Software Architecture: Companion Proceedings, page 41. ACM, 2018. [ bib | DOI ] |
[11] | R. Wen, D. Gilbert, M. G. Roche, and S. McIntosh. BLIMP Tracer: Integrating Build Impact Analysis with Code Review. In 2018 IEEE International Conference on Software Maintenance and Evolution (ICSME)s, pages 685--694. IEEE, 2018. [ bib | DOI ] |
[12] | L. B. Cuong, V. S. Nguyen, D. A. Nguyen, P. N. Hung, and D. H. Vo. JCIA: A Tool for Change Impact Analysis of Java EE Applications. In Information Systems Design and Intelligent Applications, pages 105--114. Springer, Singapore, 2018. [ bib | DOI ] |
[13] | L. Badri, M. Badri, and D. St-Yves. Supporting predictive change impact analysis: a control call graph based technique. In 12th Asia-Pacific Software Engineering Conference (APSEC'05), pages 167--175. IEEE, 2005. [ bib | DOI ] |
[14] | D. Poshyvanyk, A. Marcus, R. Ferenc, and T. Gyimóthy. Using information retrieval based coupling measures for impact analysis. Empirical Software Engineering, 14(1):5–32, 2009. [ bib | DOI ] |
[15] | A.E. Hassan and R.C. Holt. Predicting Change Propagation in Software Systems. In 20th IEEE International Conference on Software Maintenance, 2004. Proceedings, pages 284--293. IEEE, 2004. [ bib | DOI ] |
[16] | I. S. Wiese, R. Ré, I. Steinmacher, R. T. Kuroda, G. A. Oliva, C. Treude, and M. A. Gerosa. Using contextual information to predict co-changes. Journal of Systems and Software, 128:220--235, 2017. [ bib | DOI ] |
[17] | D. Falessi, J. Roll, J. Guo, and J. Cleland-Huang. Leveraging Historical Associations between Requirements and Source Code to Identify Impacted Classes. IEEE Transactions on Software Engineering, 2018. [ bib | DOI ] |
[18] | J. Novacek, A. Ahari, A. Cornaglia, F. Haxel, A. Viehl, O. Bringmann, and Wo. Rosenstiel. Ontology-Supported Design Parameter Management for Change Impact Analysis. In 2018 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), pages 9--16. IEEE, 2018. [ bib | DOI ] |
[19] | A. Parashar and J. K. Chhabra. Assessing Impact of Class Change by Mining Class Associations. The International Arab Journal of Information Technology, IAJIT, 16(1):98--107, 2019. [ bib ] |
[20] | N. Alhindawi, N. Dragan, M. L. Collard, and J. I. Maletic. Improving Feature Location by Enhancing Source Code with Stereotypes. In 2013 IEEE International Conference on Software Maintenance. IEEE, 2013. [ bib | DOI ] |
[21] | B. Dit and M. Revelleand M. Gethersand D. Poshyvanyk. Feature location in source code: A taxonomy and survey. Journal of software: Evolution and Process, 25(1):53--95, 2011. [ bib | DOI ] |