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http://krishi.icar.gov.in/jspui/handle/123456789/44610| Title: | Complexity of the Code Changes and Issues Dependent Approach to Determine the Release Time of Software Product |
| Other Titles: | Not Available |
| Authors: | V. B. Singh K. K. Chaturvedi Sujata Khatri Meera Sharma |
| ICAR Data Use Licennce: | http://krishi.icar.gov.in/PDF/ICAR_Data_Use_Licence.pdf |
| Author's Affiliated institute: | Delhi College of Arts and Commerce, University of Delhi, Delhi, India ICAR::Indian Agricultural Statistics Research Institute DDU College, University of Delhi, New Delhi, India Swami Shraddhanand College, University of Delhi, Delhi, India |
| Published/ Complete Date: | 2017-07-15 |
| Project Code: | Not Available |
| Keywords: | Entropy Complexity of code change Release problem Bug repositories Source code repositories |
| Publisher: | Springer, Cham |
| Citation: | Singh V.B., Chaturvedi K.K., Khatri S., Sharma M. (2017) Complexity of the Code Changes and Issues Dependent Approach to Determine the Release Time of Software Product. In: Gervasi O. et al. (eds) Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science, vol 10408. Springer, Cham. https://doi.org/10.1007/978-3-319-62404-4_39 |
| Series/Report no.: | Not Available; |
| Abstract/Description: | Changes in source code of the software products are inevitable. We need to change the source code to fix the feature improvements, new features and bugs. Feature improvements, new features and bugs are collectively termed as issues. The changes in the source code of the software negatively impact its product, but necessary for the evolution of the software. The changes in source code are quantified using entropy based measure and it is called the complexity of code changes. In this paper, we built regression models to predict the next release time of software using the complexity of code changes (entropy), feature improvements, new feature implementation and bugs fixed. The regression models have been built using Multiple Linear Regression (MLR), various kernel functions based Support Vector Regression (SVR) and k-Nearest Neighbor (k-NN) methods. The proposed models have been empirically validated using four open source sub-projects of the Apache software foundation. The proposed models exhibit a good fit. The developed models will assist release managers in release planning of the software. |
| Description: | Not Available |
| Type(s) of content: | Research Paper |
| Sponsors: | Not Available |
| Language: | English |
| Name of Journal: | Computational Science and Its Applications |
| NAAS Rating: | Not Available |
| Volume No.: | 10408 |
| Page Number: | 519-529 |
| Name of the Division/Regional Station: | Not Available |
| Source, DOI or any other URL: | https://doi.org/10.1007/978-3-319-62404-4_39 |
| URI: | https://doi.org/10.1007/978-3-319-62404-4_39 http://krishi.icar.gov.in/jspui/handle/123456789/44610 |
| Appears in Collections: | AEdu-IASRI-Publication |
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