Change impact analysis
Pro Research Analysisby 
Searched over 200M research papers
Change Impact Analysis: A Comprehensive Overview
Importance of Change Impact Analysis in Software Development
Change impact analysis (CIA) is a critical process in software development and maintenance, aimed at understanding the potential consequences of modifications within a system. This process helps in reducing maintenance costs and mitigating the risk of introducing new bugs or failures2 6. CIA is essential for ensuring that changes do not adversely affect other parts of the system, thereby maintaining software integrity and performance.
Methods and Techniques in Change Impact Analysis
Quantification and Metrics
CIA can be quantified using various parameters such as instability, amount of change, change proneness, and changeability. These parameters are assessed through different metrics and predictors, which help in managing the impact of changes efficiently2. The practical benefits of CIA include handling various maintenance requests like feature additions and bug fixes, ultimately reducing relevant costs2.
Dynamic vs. Static Analysis
Dynamic change impact analysis identifies potential impacts based on representative executions of the software. However, studies have shown that dynamic impact analyses can suffer from low precision and recall, with average precision ranging from 38-50% and recall from 50-56%4. On the other hand, static analysis involves examining the code without executing it, often using IDE navigational functionalities to predict impacts before making changes6.
Model-Based Approaches
Model-based impact analysis approaches, particularly for state-based systems like telecommunication or embedded systems, use model dependencies to measure the expected impact of changes. This method allows for early discovery of critical components before actual code changes, speeding up the maintenance process5.
Historical Change-Patterns
Mapping historical change-patterns to current CIA tasks can boost the accuracy of traditional CIA tools. By retrieving equivalent entities from historical change sets and comparing change-patterns, the confidence in impacted entities can be improved, leading to better accuracy in predicting change impacts3.
Challenges and Future Directions
Predictive Accuracy
The predictive accuracy of CIA, especially dynamic analysis, remains a challenge. Studies indicate that the most cost-effective dynamic impact analyses are surprisingly inaccurate, motivating the need for developing more precise techniques4.
Fine-Grained vs. Coarse-Grained Techniques
Combining coarse-grained techniques (e.g., file, class, method level) with fine-grained techniques (e.g., slicing) can improve the precision of CIA. The variable-method correlation-based CIA technique (VM-CIA) is one such approach that enhances precision while maintaining reasonable recall7.
Modular and Service-Based Approaches
In manufacturing and service-based environments, modular approaches to CIA allow for tailored analysis based on specific inputs and desired outcomes. This flexibility is crucial for addressing the diverse and dynamic nature of changes in these systems8 9.
Conclusion
Change impact analysis is a vital component of software maintenance and development, offering significant benefits in terms of cost reduction and risk management. While various methods and techniques exist, challenges such as predictive accuracy and the balance between fine-grained and coarse-grained analysis remain. Future research and development in CIA should focus on improving accuracy and developing modular, adaptable approaches to meet the evolving needs of software systems.
Sources and full results
Most relevant research papers on this topic
An Empirical Study on Views of Importance of Change Impact Analysis Issues
Change impact analysis issues are prioritized from an organizational perspective, but personal opinions and attitudes can influence this view.
Highly CitedChange impact analysis: A systematic mapping study
Change Impact Analysis (CIA) reduces maintenance costs and risks, and can be quantified using four parameters (instability, amount of change, change proneness, and changeability) to efficiently manage software development.
Change-Patterns Mapping: A Boosting Way for Change Impact Analysis
Our method improves change impact analysis accuracy by mapping historical change-patterns to current tasks, enhancing traditional tools like ImpactMiner, JRipples, and ROSE.
A comprehensive study of the predictive accuracy of dynamic change-impact analysis
Dynamic change-impact analysis is surprisingly inaccurate, with an average precision of 38-50% and recall of 50-56% in most cases.
Toward automatically quantifying the impact of a change in systems
The proposed model-based impact analysis approach automatically measures the expected impact of changes in state-based systems, allowing early discovery of critical components before applying changes at the code level.
Do Programmers do Change Impact Analysis in Debugging?
Programmers do change impact analysis during software maintenance, using IDE navigational functionalities and running programs, but do not use any change impact analysis tools.
Mining Variable-Method Correlation for Change Impact Analysis
The VM-CIA technique improves change impact analysis precision by 19% while maintaining recall, making it more practical and efficient for software developers.
Modular change impact analysis in factory systems
This paper presents a modular approach for individualizing change impact analysis in manufacturing systems, addressing the challenge of evaluating adjustments' effects on the current production system.
Change impact analysis in service-based business processes
This paper proposes an approach for analyzing change impact in service-based business processes, identifying change types and impact patterns, and providing algorithms for computing impact scopes of changes.
Change impact analysis for object-oriented programs
Change impact analysis in object-oriented programs helps maintenance programmers identify changes responsible for regression test failures and safely incorporate changes without affecting test drivers.
Highly Cited