Soft Computing Approaches for Prediction of Software Maintenance Effort

Kaur, Amandeep; Kaur, Kamaldeep; Malhotra, Ruchika

doi:10.5120/339-515

Cited by 34 publications

(16 citation statements)

References 13 publications

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“…Many neural network algorithms are used to predict reusability of object-oriented software components using refined metrics. Kaur et al [46] conducts an empirical study using eight object-oriented metrics as independent variables and maintenance efforts as dependent variable. Data of two commercial software products are used in this study.…”

Section: Other Studiesmentioning

confidence: 99%

Survey of object-oriented metrics

Sharma

Gill

Sikka

2012

SIGSOFT Softw. Eng. Notes

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To quantify various attributes of object-oriented software systems, numerous object-oriented metrics have been proposed by various researchers. This paper surveys existing object-oriented metrics by focusing on the two major issues-Validation and Formal Specification. This paper provides researchers with an understanding of existing object-oriented metrics in terms of validation and formal specification. Because few metrics are validated and formally specified, there is a need of both validation and formal specification of object-oriented metrics.

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Section: Other Studiesmentioning

confidence: 99%

Survey of object-oriented metrics

Sharma

Gill

Sikka

2012

SIGSOFT Softw. Eng. Notes

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“…They used two systems UIMS (User Interface System) and QUES (Quality Evaluation System) for their research work. [9] A. Kaur, K. Kaur and R. Malhotra show that fuzzy Inference System, adaptive neuro-fuzzy inference systems, and artificial neural network can be used to predict maintenance effort based on eight object oriented metrics [10].…”

Section: Related Workmentioning

confidence: 99%

A proposed new model for maintainability index of open source software

Kaur

Pathak

2014

Proceedings of 3rd International Conference on Reliability, Infocom Technologies and Optimization

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Software metrics play a key role in measuring attributes that are important for the success of a software project. Measurements of these metrics tell us various key aspects of system. This in turn supports knowledgeable decision making by which we can enhance the quality of system. Maintenance is a process of revisions or corrections made to software systems after their first release. The key feature of software development is change. Hence it is important to develop software that is easy to modify and is thus maintainable. This paper evaluates the existing Oman and Hagemeister maintainability index model which calculates maintainability index (MI) based on Cyclomatic Complexity, Lines of code and Halsted volume. For this purpose, software metric datasets of Lucene, which is open source software of 163085 lines of code are used, and it is shown that the existing Oman and Hagemeister maintainability index mode model is not a good a predictor of maintainability. A new maintainability index model is proposed with a new set of predictor metrics. The new proposed model is a marked improvement over the existing Oman andHagemeister maintainability index model. The coefficient of determination ( ) of the new proposed maintainability model is 0.984 and correlation coefficient(R) is 0.992 as compared to the Oman and Hagemeister model whose correlation coefficient(R) is 0.320

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“…They have evaluated the performance of the model using ROC analysis, precision and recall. Apart from this, a lot of work has also been done till date in predicting software maintainability, but all this work has been focused on analyzing the OO software design metrics and how these OO metrics can contribute in the prediction of software maintainability [11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27].…”

Section: Literature Reviewmentioning

confidence: 99%