scholarly journals Improving the Accuracy of Static Source Code Based Software Change Impact Analysis Through Hybrid Techniques: A Review

2021 ◽  
Vol 7 (1) ◽  
pp. 57-66
Author(s):  
YUSUFF SHAKIRAT ◽  
◽  
AMOS BAJEH ◽  
T.O Aro ◽  
KAYODE ADEWOLE ◽  
...  
Keyword(s):  
Software Maintenance ◽  
Impact Analysis ◽  
Optimal Level ◽  
Software Systems ◽  
Change Impact Analysis ◽  
Hybrid Techniques ◽  
Software Change ◽  
Software Changes ◽  
Systems Quality ◽  
Change Impact

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.

Architectural Slicing to Support System Evolution

2011 ◽  
pp. 197-210 ◽  
Author(s):  
Jianjun Zhao ◽  
Limin Xiang
Keyword(s):  
Software Architecture ◽  
Support System ◽  
Software Maintenance ◽  
Impact Analysis ◽  
Software Systems ◽  
Process Of Change ◽  
Change Impact Analysis ◽  
System Evolution ◽  
Software Maintenance And Evolution ◽  
Change Impact

Change impact analysis is a useful technique in software maintenance and evolution. Many techniques have been proposed to support change impact analysis at the code level of software systems, but little effort has been made for change impact analysis at the architectural level. In this chapter, we present an approach to supporting change impact analysis at the architectural level of software systems based on the architectural slicing and chopping technique. The main feature of our approach is to assess the effect of changes in a software architecture by analyzing its formal architectural specification, and therefore, the process of change impact analysis can be automated completely.

scholarly journals Change impact analysis for maintenance and evolution of variable software systems

2019 ◽  
Vol 26 (2) ◽  
pp. 417-461 ◽  
Author(s):  
Florian Angerer ◽  
Andreas Grimmer ◽  
Herbert Prähofer ◽  
Paul Grünbacher
Keyword(s):  
Impact Analysis ◽  
Software Systems ◽  
Change Impact Analysis ◽  
Change Impact

scholarly journals A Structure-Driven Method for Information Retrieval-Based Software Change Impact Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-16
Author(s):  
Yun He ◽  
Tong Li ◽  
Wei Wang ◽  
Wei Lan ◽  
Xiang Li
Keyword(s):  
Information Retrieval ◽  
Impact Analysis ◽  
Contextual Information ◽  
Source Code ◽  
Word Embedding ◽  
Bag Of Words ◽  
Change Impact Analysis ◽  
Software Change ◽  
Change Impact ◽  
Single Method

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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