Change impact analysis: A systematic mapping study

Kretsou, Maria; Arvanitou, Elvira-Maria; Ampatzoglou, Apostolos; Deligiannis, Ignatios; Gerogiannis, Vassilis C.

doi:10.1016/j.jss.2020.110892

Cited by 10 publications

(4 citation statements)

References 44 publications

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“…Also, software engineers are placing time and effort to remove code smells in cloud platforms. As a result, predicting the change in resource requirement of the cloud after refactoring code smells is important to automatically and correctly provision future resource requirements and refactoring activities [8]. It will be beneficial from both the technical and financial perspectives of the cloud service provider.…”

Section: Resource Prediction: Uregm Methodsmentioning

confidence: 99%

URegM: a unified prediction model of resource consumption for refactoring software smells in open source cloud

Imran

Kosar

2022

2022 the 3rd European Symposium on Software Engineering

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The low cost and rapid provisioning capabilities have made the cloud a desirable platform to launch complex scientific applications. However, resource utilization optimization is a significant challenge for cloud service providers, since the earlier focus is provided on optimizing resources for the applications that run on the cloud, with a low emphasis being provided on optimizing resource utilization of the cloud computing internal processes. Code refactoring has been associated with improving the maintenance and understanding of software code. However, analyzing the impact of the refactoring source code of the cloud and studying its impact on cloud resource usage require further analysis. In this paper, we propose a framework called Unified Regression Modelling (URegM) which predicts the impact of code smell refactoring on cloud resource usage. We test our experiments in a real-life cloud environment using a complex scientific application as a workload. Results show that URegM is capable of accurately predicting resource consumption due to code smell refactoring. This will permit cloud service providers with advanced knowledge about the impact of refactoring code smells on resource consumption, thus allowing them to plan their resource provisioning and code refactoring more effectively.CCS Concepts: • Software and its engineering → Maintaining software.

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Section: Resource Prediction: Uregm Methodsmentioning

confidence: 99%

URegM: a unified prediction model of resource consumption for refactoring software smells in open source cloud

Imran

Kosar

2022

2022 the 3rd European Symposium on Software Engineering

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“…For the sake of brevity, we will focus on presenting the most relevant research works that used code metrics and changelogs data. For more details about techniques and methods used in the field, please refer to the systematic literature reviews done such as [8], [9].…”

Section: Related Workmentioning

confidence: 99%

Enhancing Software Co-Change Prediction: Leveraging Hybrid Approaches for Improved Accuracy

Zagane,

Alenezi

2024

IEEE Access

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Accurate prediction of co-changes in software systems is crucial for efficient development and maintenance, especially as systems grow in complexity. While deep learning-based approaches have shown promise, they often struggle with diverse and complex data. In this paper, we present a novel hybrid approach that combines traditional software engineering methods with deep learning techniques to improve co-change prediction accuracy. Our approach leverages software metrics and deep learning models, incorporating the unique characteristics induced by naming conventions, such as PascalCase and camelCase, used by developers for naming consistency. By utilizing char n-gram embedding and sub-token context, we enrich the vector representations of source file names, capturing relationships and dependencies between files. We comprehensively evaluate our hybrid approach using three open-source software projects. The findings of this study have significant implications for the development of more effective software co-change prediction tools and techniques, enabling better decision-making in software development and maintenance processes. Our approach outperforms traditional software engineering methods and deep learning-based approaches, demonstrating its potential to significantly improve software development and maintenance efficiency.

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“…A. Theoretical Foundations a) Change Impact Analysis (CIA): CIA refers to the process of exploring the effects of a given change in other parts of the system [10]. A natural way to analyze change impact is to raise the following questions: If I change this design element or this requirement, what are the elements that will be affected and must be modified in turn?…”

Section: Theoretical Foundations and Related Workmentioning

confidence: 99%

“…b) Traceability: In practice, change impact analysis relies on traceability defined as "a discernible association among two or more logical entities such as requirements, system elements, verifications, or tasks" [15]. Albeit traceability is considered as one of the most frequently adopted techniques for change impact analysis [10], it has been argued that a traceability model "is not sufficient to capture the progress of evolving artifacts" [16] as elements at the architectural design may evolve to a new configuration of architectural elements [17]. In view of this, to perform change impact analysis, it is not sufficient to define a traceability representation language and hence to create traceability models.…”

Section: Theoretical Foundations and Related Workmentioning

confidence: 99%

On the Conceptualization of Tools for Traceability-Based Change Impact Analysis Using a Domain-Specific Modeling Approach

Awad,

Boyer

2023

2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C)

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In the context of system architecture design, design changes are deemed as an important issue in the field of cyber-physical systems. This is due to their costs that can increase up to several folds with each step of the life cycle, from conceptual design to operation. Accordingly, before putting such changes into practice, their impacts need to be analyzed based on traceability of dependencies among elements in system artifacts. However, traceability-based Change Impact Analysis (CIA) is time consuming and error-prone process, if performed manually. Thus, a number of tools have been developed in this regard. Yet, in the absence of approaches guiding the specification and implementation of such tools, research reveals their inadequacy for the needs of the industry. To overcome this, this paper proposes a domain-specific modeling approach for the conceptualization of traceability-based CIA tools. Such approach is composed of three building blocks. First, a language for traceability-based CIA designed around the concept of "CIA parameters" (i.e., all kind of information that could contribute to quantitative or qualitative change impact assessment). Second, a modeling procedure referring to a multi-view modeling approach that explains how to use the traceability language to allow creation and machine processing of models (i.e., analysis of models in terms of change impact). Third, mechanisms and algorithms focusing on the implementation of the modeling procedure on a metamodeling platform. The proposed approach is thus an attempt to bridge the gap between design and implementation of traceability-based CIA tools. The approach is illustrated on an example from the aviation field.

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Change impact analysis: A systematic mapping study

Cited by 10 publications

References 44 publications

URegM: a unified prediction model of resource consumption for refactoring software smells in open source cloud

URegM: a unified prediction model of resource consumption for refactoring software smells in open source cloud

Enhancing Software Co-Change Prediction: Leveraging Hybrid Approaches for Improved Accuracy

On the Conceptualization of Tools for Traceability-Based Change Impact Analysis Using a Domain-Specific Modeling Approach

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