Comparative Analysis of Object-Oriented Software Maintainability Prediction Models

Abstract

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.

Impact of Historical Software Metric Changes in Predicting Future Maintainability Trends in Open-Source Software Development

“…The relationship between software design metrics and their maintainability has been proposed and validated by many researchers [6,9]. Based on the empirical study by Malhotra and Chug, it has been established that the quality of the software design, as well as code, is very important to enhance software maintainability [9].…”

“…The indirect maintainability measures combined with a variety of software metrics that capture the quality of software's internal quality, represent efficient input for either statistical or machine learning algorithms to make useful prediction models. To establish a relationship between software design metrics as the independent variable and maintainability as the dependent variable, various techniques have been practised in the last two and half decades [9], including statistical algorithms, machine learning algorithms, nature-inspired techniques, expert judgment, and hybrid techniques [6,19].…”

“…Consequently, one of the essential objectives of software engineering is to develop techniques and tools for high-quality software solutions that are stable and maintainable [5]. Software maintainability is one of the most important aspects when evaluating the quality of a software product [6] and is one of key stages in the software development lifecycle [7].…”

An Analysis of Metric-based Quality Design for Software Development

“…Object-Oriented metrics are an estimation procedure of product metrics in which computation is done on real-world entities to depict them as indicated by plainly characterized rules. These metrics encourage programming specialists to discover the profitability of the product application (6) .…”

Maintainability Prediction for Object-Oriented Software Systems Based on Intelligent Techniques: Literature Review

“…Most of these models were suggested in the level of code while a few models were suggested at levels of design and architecture [8]. In this paper, we will be considering the most recent proposed techniques to predict Object-Oriented software maintainability utilizing artificial intelligent techniques.…”