scholarly journals Software Change Management: a Quantified Perspective

2018 ◽  
Vol 7 (3.12) ◽  
pp. 963
Author(s):  
Ankit Dhamija ◽  
Sunil Sikka

A systematic Change Impact Analysis (CIA) is being used for better change management of software. Also, CIA process is evolved continuously to make it more effective. Software metrics play an important role to evaluate CIA process. Two types of metrics are used to evaluate CIA. First types of metrics are the standard metrics used to evaluate the performance of CIA techniques for example Precision, Recall, F-measure etc. These are most commonly used by researchers. Second types of metrics are those which are used to quantify the change impact which is based on the code/design features. This paper is aimed at identification of these second types of metrics available in literature.  

Author(s):  
YUSUFF SHAKIRAT ◽  
◽  
AMOS BAJEH ◽  
T.O Aro ◽  
KAYODE ADEWOLE ◽  
...  

Change is an inevitable phenomenon of life. This inevitability of change in the real world has made a software change an indispensable characteristic of software systems and a fundamental task of software maintenance and evolution. The continuous evolution process of software systems can greatly affect the systems’ quality and reliability if proper mechanisms to manage them are not adequately provided. Therefore, there is a need for automated techniques to effectively make an assessment of proposed software changes that may arise due to bug fixes, technological advancements, changing user requirements etc., before their implementation. Software Change Impact Analysis (CIA) is an essential activity for comprehending and identifying potential change impacts of software changes that can help prevent the system from entering into an erroneous state. Despite the emergence of different CIA techniques, they are yet to reach an optimal level of accuracy desired by software engineers. Consequently, researchers in recent years have come up with hybrid CIA techniques which are a blend of multiple CIA approaches, as a way of improving the accuracy of change impacts analysis techniques. This study presents these hybrid CIA techniques and how they improve accuracy. They are also compared and areas for further research are identified.


Author(s):  
Sha Ma ◽  
Bin Song ◽  
Wen Feng Lu ◽  
Cheng Feng Zhu

Engineering changes are inevitable in a product development life cycle. The requests for engineering changes can be due to new customer requirements, emergence of new technology, market feedback, or variations of components and raw materials. Each change generates a level of impact on costs, time to market, tasks and schedules of related processes, and product components. Change management tools available today focus on the management of document and process changes. Assessments of change impact are typically based on the “rule of thumb”. Our research has developed a methodology and related techniques to quantify and analyze the impact of engineering changes to enable faster and more accurate decision-making in engineering change management. Reported in this paper are investigations of industrial requirements and fundamental issues of change impact analysis as well as related research and techniques. A framework for a knowledge-supported change impact analysis system is proposed. Three critical issues of system implementation, namely integrated design information model, change plan generator and impact estimation algorithms, are addressed. Finally the benefits and future work are discussed.


2018 ◽  
Vol 2018 ◽  
pp. 1-16
Author(s):  
Yun He ◽  
Tong Li ◽  
Wei Wang ◽  
Wei Lan ◽  
Xiang Li

An important application of information retrieval technology is software change impact analysis. Existing information retrieval-based change impact analysis methods select a single method to transform the source code corpus into vectors in a process known as indexing. The single method is chosen from two primary methods, known as the bag-of-words and word embedding models, each having their specific advantages and disadvantages. The bag-of-words model records every word in the source code but ignores contextual information in the corpus. The word embedding model records the contextual information but loses detail for individual words. To address this problem, we propose a structure-driven method for information retrieval-based change impact analysis (named SDM-CIA). SDM-CIA integrates the bag-of-words and word embedding models based on the software’s structure. Our experiments using a standard benchmark shows that when compared with the existing methods, SDM-CIA improves on precision performance, recall performance, F-score performance, and MRR performance by an average of 3.65%, 3.82%, 3.6%, and 10.28%, respectively. Our experiments confirm the effectiveness of SDM-CIA.


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