Implementation of Change Management in Software Development by using Scrum Framework

Author(s):  
Rupali Pravinkumar Pawar ◽  
Kirti Nilesh Mahajan

This paper will focus on implementation of change management in Scrum software development framework . Scrum is one of the mostly used software development framework from the Agile software development methodology. Scrum is based on iterative and incremental process. It is suitable for unstable  requirements. The use of Scrum proved to be beneficial due to tight schedule and loosely defined user requirements that often changed during the development. The aim of the paper to study  implementation process of change management in  Scrum . First part of paper gives detailed information of Scrum framework.  The middle of the paper presented the organizational process of agile software development using Scrum. Finally, the paper point out key points for managing changes in Scrum implementation.  The primary data collection method was interviews of the industry expertise. The secondary source of data is reference books and Internet articles. This paper will help to understand basics of Scrum software development framework and process of change management in developing projects by using Scrum framework.

2022 ◽  
Vol 10 (1) ◽  
pp. 0-0

Software failure prediction is an important activity during agile software development as it can help managers to identify the failure modules. Thus, it can reduce the test time, cost and assign testing resources efficiently. RapidMiner Studio9.4 has been used to perform all the required steps from preparing the primary data to visualizing the results and evaluating the outputs, as well as verifying and improving them in a unified environment. Two datasets are used in this work, the results for the first one indicate that the percentage of failure to predict the time used in the test is for all 181 rows, for all test times recorded, is 3% for Mean time between failures (MTBF). Whereas, SVM achieved a 97% success in predicting compared to previous work whose results indicated that the use of Administrative Delay Time (ADT) achieved a statistically significant overall success rate of 93.5%. At the same time, the second dataset result indicates that the percentage of failure to predict the time used is 1.5% for MTBF, SVM achieved 98.5% prediction.


2020 ◽  
Vol 30 (2) ◽  
pp. 100288 ◽  
Author(s):  
Anna Zaitsev ◽  
Uri Gal ◽  
Barney Tan

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