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.

Comparative Analysis of Object-Oriented Software Maintainability Prediction Models

For most software systems, superfluous software metrics are often collected. Sometimes, metrics that are collected may be redundant or irrelevant to fault prediction results. Feature (software metric) selection helps separating relevant software metrics from irrelevant or redundant ones, thereby identifying the small set of software metrics that are best predictors of fault proneness for new components, modules, or releases. In this study, we compare three forms of feature selection techniques (filter-and wrapper-based subset evaluators along with two search techniques (Best First (BF) and Greedy Stepwise (GS)), and feature ranking on four datasets from a real world software project. Five learners are used to build fault prediction models with the selected software metrics. Each model is assessed using the Area Under the Receiver Operating Characteristic Curve (AUC). We find that wrapper-based subset evaluators performed best and feature ranking performed worst. In addition, the model built with the logistic regression (LR) learner performs best in terms of the AUC performance metric. This leads us to recommend the use of the wrapper-based subset evaluators to select software metric subsets and the LR learner for building software fault prediction models.

A Study on Software Metric Selection for Software Fault Prediction

Software quality estimation is an important aspect as it eliminates design and code defects. Objectoriented quality metrics prediction can help in the estimation of software quality of any defects and the chances of errors. In this paper a survey and the case analytics have been presented for the object-oriented quality prediction. It shows the analytical and experimental aspects of previous methodologies. This survey also elaborates different object-oriented parameters which is useful for the same problem. It also elaborates the problem aspects as well the limitations for the future directions. Machine learning and artificial intelligence methods have been considered mostly for this survey. The parameters considered are inheritance, dynamic behavior, encapsulation, objects etc.

https://www.accentsjournals.org/PaperDirectory/Journal/TIS/2020/1/1.pdf

Object oriented quality prediction through artificial intelligence and machine learning: a survey

In this paper an efficient logistic regression and ant colony optimization-based object-oriented quality prediction has been presented. The dataset has been considered based on the object-oriented codes. The code considered here are completely equipped with object-oriented features for the complete analysis. The major parameters considered for the experimentation are class, object, inheritance and dynamic behavior. Class and objects have been considered for the class labels and memory requirements checking with the proper stack call and constructor invocation. Inheritance has been considered for the code reusability testing. Dynamic behavior has been tested for the runtime allocation. Then clustering algorithm is used for the parameter preprocessing and grouping based on the parameters. For data filtration chi-square testing has been applied. Then logistic regression and ant colony optimization (LR-ACO) have been considered for the final classification. Then F-Measure, Odd Ratio and Power have been considered for the analysis of the classification based on LR-ACO. The result after LR-ACO shows better accuracy as comparison to the previous methods.

https://www.accentsjournals.org/PaperDirectory/Journal/TIS/2020/4/1.pdf

An efficient logistic regression and ant colony optimization-based object-oriented quality prediction

Software Maintainability is an indispensable characteristic to determine software quality. It can be described as the ease with which necessary changes such as fault correction, performance improvement, addition, or deletion of one or more attributes, etc., can be incorporated. A major purpose of software maintainability is to enable the software to adapt to the changing environment. Machine Learning (ML) algorithms are widely used for Software Maintainability Prediction (SMP). Hence, in the current study, QUES and UIMS, i.e., the two object-oriented datasets are used for SMP. In this study, an attempt has been made to improve the prediction results of five (ML) algorithms, viz., General Regression Neural Network (GRNN), Regularized Greedy Forest (RGF), Gradient Boosting Algorithm (GBA), Multivariate Linear Regression (MLR), and K-Nearest Neighbor (k-NN) on using three different feature selection methods, including the Pearson's Correlation (Filter Method), Backward Elimination (Wrapper Method), and Lasso Regularization (Embedded Method). Feature selection is a procedure to select a set of independent variables that contribute most to the predicted output, hence eliminating the irrelevant features in the data that may reduce the accuracy of an algorithm. The performance of all the models is evaluated using three accuracy measures, i.e., R-Squared, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The results portray an improvement in the prediction accuracies after employing feature selection techniques. It is observed that for the QUES dataset, R-Squared value on an average improves by 157.89%. Also, MAE and RMSE values enhance by 19.59% and 24.90%, respectively, depicting an overall decrease in the error. Similarly, for UIMS dataset, R-Squared value on an average increase by 126.08%, representing an improvement in the accuracy. Further, MAE and RMSE values also improve for the UIMS dataset, by 12.44% and 8.16%, respectively.

Development of Efficient and Optimal Models for Software Maintainability Prediction using Feature Selection Techniques

Software quality is the ease with which a process or software fulfills customer's expectation. Software metrics have become an integral part of software development process to measure software quality quantitatively. However, not all the metrics affect quality in the same way. Selecting the relevant features reduces the time of computation and hence, improves efficiency. In this paper, different object-oriented metrics have been collected and then the most relevant metrics/features that affect maintainability in terms of CHANGE have been found using five feature selection techniques and three machine learning models. Additionally, the results have been compared with existing work and observed that results improve by selecting only the relevant features for prediction.

http://tudr.thapar.edu:8080/jspui/bitstream/10266/4628/4/4628.pdf

Evaluation of Feature Selection Techniques for Software Maintenance Prediction

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Evaluation of Feature Selection Techniques for Software Maintenance Prediction