Using Design of Experiments to Analyze Open Source Software Metrics for Change Impact Estimation

Dahane, Miloud; Abdi, Mustapha Kamel; Bouneffa, Mourad; Ahmad, Adeel; Basson, Henri

doi:10.4018/ijossp.2019010102

Cited by 4 publications

(3 citation statements)

References 26 publications

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“…Several works have been carried out in this direction. We refer, for instance, an approach in [2] which proposes a model based on mathematical equations making it possible to estimate the cost of the change of a given object-oriented software with regard to these measurements through a modeling of the relation between the change impact and the metrics known as coupling. In the same way, the authors in [3] propose a model which shows the importance of the metrics through their bonds with the attributes of the object-oriented software quality.…”

Section: Related Workmentioning

confidence: 99%

Predictive approach to the degree of business process change

Cherif,

Abdi,

Ahmed

et al. 2023

IJCDS

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Computer systems must respond to frequently changing user needs in order to remain operational. Their increasing size and operational complexities intend to make them difficult to maintain. A change in a business process is a complicated task especially during the process execution, where a small change can significantly affect the rest of the system with undesirable impacts. In this work, we focus on studying the problem of change impact propagation in Business Process Management (BPM). We propose in this paper an approach that can predict the level of change (LC) in business models. There are three level of changes (Low, Medium, and High) based on structural metrics as used in the predictive model. Five different machine learning (ML) algorithms are used in this model to show their comparison analysis. This issue is important as the LC in business process before implementing any changes helps the organization to make decision in prior. To validate the purposed approach, the experiments conducted in this study show the improved performance using the SVM and Guassian Naïve Bayes algorithms.

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Section: Related Workmentioning

confidence: 99%

Predictive approach to the degree of business process change

Cherif,

Abdi,

Ahmed

et al. 2023

IJCDS

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show abstract

“…More a class is coupled with other classes, more is important its sensibility for the changes in these classes. Good quality software has to obey the principle of weak coupling (Chidamber & Kemerer, 1994, Dahane et al, 2019. A weak coupling facilitates the maintenance seen that dependencies between the classes are small.…”

Section: Coupling Vs Impact Analysismentioning

confidence: 99%

Towards Group Decision Support in the Software Maintenance Process

Zoheir

Abdi

2022

International Journal of Decision Support System Technology

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Software maintenance is an element-key of the life cycle of software. However,the techniques of software maintenance do not consider the diversity and the complexity of decisions which do not stop increasing. So, there are at present a few tools, susceptible to insure the relevance and the efficiency of the decision-making in this phase. The work presented in this paper aims to eliminate or at least to reduce the effect to fall in an expensive change by reducing the time to find a compromise on the adequate change. The development of the decision support system for software maintenance is an answer to the problem. The developed tool allows:-to make a fast diagnosis on the software by using the coupling metrics;-to help the decision-makers of the maintenance, according to their preferences often conflicting, to adopt a change among several proposed. To answer this group decision where various points of view are considered, we propose a negotiation protocol. This protocol try to find a compromise that suits best all the decision-makers.

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“…Mining change patterns from revision history using association rules is a viable solution and more related studies are needed to discover improvement techniques for prediction results. Most of the proposed studies [6, 9, 11, 13, 14, 22, 25–27] consider all the old and new change commits equivalent in their prediction behaviour. Recent studies [15, 16, 28, 29] recommend emphasising recent changes, as they signify instability and can improve co‐change prediction probability.…”

Section: Related Workmentioning

confidence: 99%

Predicting co‐change probability in software applications using historical metadata

Agrawal

Singh

2020

IET softw.

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It is quite challenging to track the after‐effects of changes with increased dependencies among classes while making a change in software applications. Software change impact analysis aims to identify classes affected by a change in software applications. In recent years, researchers have found that revision history has great potential for identifying evolutionary coupling. The two main factors affecting the prediction results are the length and the age of change history considered for deriving dependencies. The effect of age of change history on co‐change prediction results in software applications is empirically studied by varying the weightage of change commits. The results indicate that the older commits have less influence in deriving dependent classes than the newer ones. The proposed approach is useful for software effort estimation and identifying dependencies during the software development, testing, and maintenance phase.

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Using Design of Experiments to Analyze Open Source Software Metrics for Change Impact Estimation

Cited by 4 publications

References 26 publications

Predictive approach to the degree of business process change

Predictive approach to the degree of business process change

Towards Group Decision Support in the Software Maintenance Process

Predicting co‐change probability in software applications using historical metadata

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