Maintainability Estimation of Object Oriented Software: Design Phase Perspective

Soni, Nupur; Khaliq, Mazhar

doi:10.17148/ijarcce.2015.43130

Cited by 3 publications

(2 citation statements)

References 20 publications

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“…Whereas Soni and al [34], had developed three models to compute maintainability prediction for class diagram using Extendibility and Reusability of the class diagrams. The maintainability is measured in terms of their extendibility and reusability.…”

Section: Prediction Models At Design Levelmentioning

confidence: 99%

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Comparative Analysis of Object-Oriented Software Maintainability Prediction Models

Zighed

Bounour

Seriai

2018

Foundations of Computing and Decision Sciences

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Software maintainability is one of the most important aspects when evaluating the quality of a software product. It is defined as the ease with which the existing software can be modified. In the literature, several researchers have proposed a large number of models to measure and predict maintainability throughout different phases of the Software Development Life Cycle. However, only a few attempts have been made for conducting a comparative study of the existent proposed prediction models. In this paper, we present a detailed classification and conduct a comparative analysis of Object-Oriented software maintainability prediction models. Furthermore, we considered the aforementioned proposed models from three perspectives, which are architecture, design and code levels. To the best of our knowledge, such an analysis that comprises the three levels has not been conducted in previous research. Moreover, this study hints at certain fundamental basics concerning the way of how measure the maintainability knowing that at each level the maintainability will be measured differently. In addition, we will focus on the strengths and weaknesses of these models. Consequently, the comparative study yields that several statistical and machine learning techniques have been employed for software maintainability prediction at code level during the last decade, and each technique possesses its specific characteristic to develop an accurate prediction model. At the design level, the majority of the prediction models measured maintainability according to the characteristics of the quality models. Whereas at the architectural level, the techniques adopted are still limited and only a few of studies have been conducted in this regard.

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Section: Prediction Models At Design Levelmentioning

confidence: 99%

“…The authors correlates class stability with maintainability effort measured by the number of hours spent on maintenance activities and by the line of code changes [34] The authors estimate the maintainability of class diagrams in respect of their Extendibility.…”

Section: Q4: How Can We Judge the Performance Of Prediction Models?mentioning

confidence: 99%

Comparative Analysis of Object-Oriented Software Maintainability Prediction Models

Zighed

Bounour

Seriai

2018

Foundations of Computing and Decision Sciences

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Software Maintainability: Systematic Literature Review and Current Trends

Malhotra

Chug

2016

Int. J. Soft. Eng. Knowl. Eng.

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Software maintenance is an expensive activity that consumes a major portion of the cost of the total project. Various activities carried out during maintenance include the addition of new features, deletion of obsolete code, correction of errors, etc. Software maintainability means the ease with which these operations can be carried out. If the maintainability can be measured in early phases of the software development, it helps in better planning and optimum resource utilization. Measurement of design properties such as coupling, cohesion, etc. in early phases of development often leads us to derive the corresponding maintainability with the help of prediction models. In this paper, we performed a systematic review of the existing studies related to software maintainability from January 1991 to October 2015. In total, 96 primary studies were identified out of which 47 studies were from journals, 36 from conference proceedings and 13 from others. All studies were compiled in structured form and analyzed through numerous perspectives such as the use of design metrics, prediction model, tools, data sources, prediction accuracy, etc. According to the review results, we found that the use of machine learning algorithms in predicting maintainability has increased since 2005. The use of evolutionary algorithms has also begun in related sub-fields since 2010. We have observed that design metrics is still the most favored option to capture the characteristics of any given software before deploying it further in prediction model for determining the corresponding software maintainability. A significant increase in the use of public dataset for making the prediction models has also been observed and in this regard two public datasets User Interface Management System (UIMS) and Quality Evaluation System (QUES) proposed by Li and Henry is quite popular among researchers. Although machine learning algorithms are still the most popular methods, however, we suggest that researchers working on software maintainability area should experiment on the use of open source datasets with hybrid algorithms. In this regard, more empirical studies are also required to be conducted on a large number of datasets so that a generalized theory could be made. The current paper will be beneficial for practitioners, researchers and developers as they can use these models and metrics for creating benchmark and standards. Findings of this extensive review would also be useful for novices in the field of software maintainability as it not only provides explicit definitions, but also lays a foundation for further research by providing a quick link to all important studies in the said field. Finally, this study also compiles current trends, emerging sub-fields and identifies various opportunities of future research in the field of software maintainability.

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Traceability Assessment Model to Estimate Quality of the Effective E-Procurement Process in Adoption

Saxena¹,

Agarwal²

2018

IJETT

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Maintainability Estimation of Object Oriented Software: Design Phase Perspective

Cited by 3 publications

References 20 publications

Comparative Analysis of Object-Oriented Software Maintainability Prediction Models

Comparative Analysis of Object-Oriented Software Maintainability Prediction Models

Software Maintainability: Systematic Literature Review and Current Trends

Traceability Assessment Model to Estimate Quality of the Effective E-Procurement Process in Adoption

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