Maintainability Prediction Models Research Articles

A Hyper-parameter Tuning based Novel Model for Prediction of Software Maintainability

Software maintainability is regarded as one of the most important characteristics of any software system. In today's digital world, the expanding significance of software maintenance is motivating the development of efficient software maintainability prediction (SMP) models using statistical and machine learning methods. This study proposes a hyper-parameter optimizable Software Maintainability Prediction (HPOSMP) model using the hybridized approach of data balancing and hyper-parameter optimization of Machine Learning (ML) approach using software maintainability datasets. The training dataset has been created with object-oriented software namely UIMS and QUES. To balance the dataset, Synthetic Minority Oversampling Technique (SMOTE) technology has been adopted. Further, Decision Tree, Gaussian Naïve Bayes, K-Nearest neighbour, Logistic Regression, and Support Vector Machine are adopted as Machine Learning and Statistical Regression Techniques for training of software maintainability dataset. Results demonstrate that the proposed HPOSMP model gives better performance as compared to the base SMP models.

A multiple criteria decision analysis based approach to remove uncertainty in SMP models

Software has to be updated frequently to match the customer needs. If software maintainability is not given priority, it affects the software development life cycle and maintenance expenses, which deplete organizational assets. Before releasing software, maintainability must be estimated, as the impact of bugs and errors can affect the cost and reputation of the organization after deployment. Regardless of the programming paradigm, it’s important to assess software maintainability. Many software maintainability prediction models’ compatibilities with new programming paradigms are criticized because their limited applicability over heterogeneous datasets. Due this challenge small and medium-sized organizations may even skip the maintainability assessment, resulting in huge lose to such organizations. Motivated by this fact, we used Genetic Algorithm optimized Random Forest technique (GA) for software maintainability prediction models over heterogeneous datasets. To find optimal model for software maintainability prediction, the Technique for Order preference by Similarity to Ideal Solution (TOPSIS), a popular multiple-criteria decision-making model, is adopted. From the results, it is concluded that the GA is optimal for predicting maintainability of software developed in various paradigms.

Software Quality Prediction Using Machine Learning

With the emergence of Machine Learning, many companies are increasingly embracing this revolutionary approach, both in terms of growth and maintenance, to reduce software costs. This research aimed at building two models which is Software Defect Prediction Model (SDPM) which will be used to predict defects in software and Software Maintainability Prediction Model (SMPM) which will be used for Software Maintainability. Different classifiers, namely Random Forest, Decision Tree, Naïve Bayes and Artificial Neural Networks have been considered and then evaluated using different metrics such as Accuracy, Precision, Recall and Area Under the Curve (AUC). The two models have successfully been evaluated and Decision Tree has been chosen as compared to other classifiers which tends to perform much better. Finally a framework based on a set of guidelines that can be used to improve software quality has been devised.

A feature selection strategy for improving software maintainability prediction

Software maintainability is a significant contributor while choosing particular software. It is helpful in estimation of the efforts required after delivering the software to the customer. However, issues like imbalanced distribution of datasets, and redundant and irrelevant occurrence of various features degrade the performance of maintainability prediction models. Therefore, current study applies ImpS algorithm to handle imbalanced data and extensively investigates several Feature Selection (FS) techniques including Symmetrical Uncertainty (SU), RandomForest filter, and Correlation-based FS using one open-source, three proprietaries and two commercial datasets. Eight different machine learning algorithms are utilized for developing prediction models. The performance of models is evaluated using Accuracy, G-Mean, Balance, & Area under the ROC Curve. Two statistical tests, Friedman Test and Wilcoxon Signed Ranks Test are conducted for assessing different FS techniques. The results substantiate that FS techniques significantly improve the performance of various prediction models with an overall improvement of 18.58%, 129.73%, 80.00%, and 45.76% in the median values of Accuracy, G-Mean, Balance, & AUC, respectively for all the datasets taken together. Friedman test advocates the supremacy of SU FS technique. Wilcoxon Signed Ranks test showcases that SU FS technique is significantly superior to the CFS technique for three out of six datasets.

An empirical study on predictability of software maintainability using imbalanced data

In software engineering predictive modeling, early prediction of software modules or classes that possess high maintainability effort is a challenging task. Many prediction models are constructed to predict the maintainability of software classes or modules by applying various machine learning (ML) techniques. If the software modules or classes need high maintainability, effort would be reduced in a dataset, and there would be imbalanced data to train the model. The imbalanced datasets make ML techniques bias their predictions towards low maintainability effort or majority classes, and minority class instances get discarded as noise by the machine learning (ML) techniques. In this direction, this paper presents empirical work to improve the performance of software maintainability prediction (SMP) models developed with ML techniques using imbalanced data. For developing the models, the imbalanced data is pre-processed by applying data resampling methods. Fourteen data resampling methods, including oversampling, undersampling, and hybrid resampling, are used in the study. The study results recommend that the safe-level synthetic minority oversampling technique (Safe-Level-SMOTE) is a useful method to deal with the imbalanced datasets and to develop competent prediction models to forecast software maintainability.

An empirical study to investigate the impact of data resampling techniques on the performance of class maintainability prediction models

With the increasing complexity of the software systems nowadays, the trend has been shifted to object-oriented (OO) development. The classes are the central construct in an OO software that are expected to be of utmost quality and high maintainability. The maintainability of a class is the probability that a class can be effortlessly modifiable in the maintenance phase. Unfortunately, it is very tough to determine the maintainability of a class with confidence before the release of the software. However, maintainability can be predicted with the help of internal quality attributes (viz. complexity, cohesion coupling, inheritance, etc.). The researchers in the literature have studied the relation amongst the internal quality attributes and class maintainability. Many class maintainability prediction models have been developed in the past with the help of internal quality attributes. Effective prediction models are vital to forecast class maintainability accurately. However, various datasets used to build prediction models for class maintainability suffer from imbalanced data problem. In that scenario, a model trained with imbalanced data gives erroneous predictions of class maintainability, which results in the inaccurate allocation of testing and maintenance resources to the misclassified classes. Therefore towards this direction, this study assesses the applicability of techniques to take care of imbalanced data. In this study the imbalanced data is treated with nine oversampling and three undersampling methods. A comprehensive comparison of fourteen machine learning (ML) techniques and fourteen search based (SB) techniques is conducted for class maintainability prediction. The results of the study support the applicability Safe-Level Synthetic Minority Oversampling Technique (Safe-SMOTE) to handle the imbalanced data for class maintainability prediction.

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

Software maintenance is one of the key stages in the software lifecycle and it includes a variety of activities that consume the significant portion of the costs of a software project. Previous research suggest that future software maintainability can be predicted, based on various source code aspects, but most of the research focuses on the prediction based on the present state of the code and ignores its history. While taking the history into account in software maintainability prediction seems intuitive, the research empirically testing this has not been done, and is the main goal of this paper. This paper empirically evaluates the contribution of historical measurements of the Chidamber & Kemerer (C&K) software metrics to software maintainability prediction models. The main contribution of the paper is the building of the prediction models with classification and regression trees and random forest learners in iterations by adding historical measurement data extracted from previous releases gradually. The maintainability prediction models were built based on software metric measurements obtained from real-world open-source software projects. The analysis of the results show that an additional amount of historical metric measurements contributes to the maintainability prediction. Additionally, the study evaluates the contribution of individual C&K software metrics on the performance of maintainability prediction models.

A systematic literature review on empirical studies towards prediction of software maintainability

Software maintainability prediction in the earlier stages of software development involves the construction of models for the accurate estimation of maintenance effort. This guides the software practitioners to manage the resources optimally. This study aims at systematically reviewing the prediction models from January 1990 to October 2019 for predicting software maintainability. We analyze the effectiveness of these models according to various aspects. To meet the goal of the research, we have identified 36 research papers. On investigating these papers, we found that various machine learning (ML), statistical (ST), and hybridized (HB) techniques have been applied to develop prediction models to predict software maintainability. The significant finding of this review is that the overall performance of ML-based models is better than that of ST models. The use of HB techniques for prediction of software maintainability is limited. The results of this review revealed that software maintainability prediction (SMP) models developed using ML techniques outperformed models developed using ST techniques. Also, the prediction performance of few models developed using HB techniques is encouraging, yet no conclusive results about the performance of HB techniques could be reported because different HB techniques are applied in a few studies.

Using Ensembles for Class-Imbalance Problem to Predict Maintainability of Open Source Software

To facilitate software maintenance and save the maintenance cost, numerous machine learning (ML) techniques have been studied to predict the maintainability of software modules or classes. An abundant amount of effort has been put by the research community to develop software maintainability prediction (SMP) models by relating software metrics to the maintainability of modules or classes. When software classes demanding the high maintainability effort (HME) are less as compared to the low maintainability effort (LME) classes, the situation leads to imbalanced datasets for training the SMP models. The imbalanced class distribution in SMP datasets could be a dilemma for various ML techniques because, in the case of an imbalanced dataset, minority class instances are either misclassified by the ML techniques or get discarded as noise. The recent development in predictive modeling has ascertained that ensemble techniques can boost the performance of ML techniques by collating their predictions. Ensembles themselves do not solve the class-imbalance problem much. However, aggregation of ensemble techniques with the certain techniques to handle class-imbalance problem (e.g., data resampling) has led to several proposals in research. This paper evaluates the performance of ensembles for the class-imbalance in the domain of SMP. The ensembles for class-imbalance problem (ECIP) are the modification of ensembles which pre-process the imbalanced data using data resampling before the learning process. This study experimentally compares the performance of several ECIP using performance metrics Balance and g-Mean over eight Apache software datasets. The results of the study advocate that for imbalanced datasets, ECIP improves the performance of SMP models as compared to classic ensembles.

Optimized neuro-PSO-based software maintainability prediction using relief features selection method

<p>The recent development in software engineering reveals the importance of software maintenance during the time of software development that is becoming more important in software development environment and software metrics, which are very essential for measuring the maintainability of software, software complexity, estimating size, quality and project efforts. There are various approaches through which one can estimate the software cost and predict on various kinds of deliverable items. This paper aims at developing an optimized Neuro-PSO-based software maintainability prediction model by applying the dimensionality reduction using relief feature selection method for identifying the optimal feature subsets in order to increase the accuracy and reduce the time complexity of the prediction model. The simulation result proves the performance of the proposed model which will be more beneficial for the software developers in predicting the maintenance of the software in advance.</p>

Estimation of maintainability parameters for object-oriented software using hybrid neural network and class level metrics

The various software metrics proposed in the literature can be used to evaluate the quality of software systems written in object-oriented manner. These metrics are broadly categorized into two subcategories i.e., system level software metrics and class level software metrics. In this work, ten different types of class level metrics are considered as an input to develop one model for predicting software maintainability of object-oriented software system. These models are developed using three types of neural networks, i.e., artificial neural network, radial basis function network, and functional link artificial neural network. In this study, a hybrid algorithm based on genetic algorithm (GA) with gradient descent algorithm has been proposed to find optimal weights of these neural networks. Since accuracy of the prediction model is highly dependent on the class level metrics, they are considered as input of the models. So, five different feature selection techniques are used in this study to identify the best set of features with an objective to improve the accuracy of software maintainability prediction model. The effectiveness of these models are evaluated using four evaluation metrics, i.e., MAE, MMRE, RMSE, and SEM. In this work, parallel computing concept has been also considered with an objective to reduce the model training time. The results show that the model developed using the proposed hybrid algorithm based on GA with gradient descent algorithm give better results as compared to the work presented by other authors in literature. The results also show that feature selection techniques obtain better results for predicting maintainability as compared to all metrics. The experimental results show that parallel computing is beneficial in reducing the model training time.

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.

Software maintainability prediction using hybrid neural network and fuzzy logic approach with parallel computing concept

In present day scenario, majority of software companies use object-oriented concept to develop software systems as it enables effective design, development, testing and maintenance, in addition to the optimal characterization of the software system. With the increase in number of these software systems, their effective maintenance aspect becomes very important day by day. In this study, Neuro-Fuzzy approach: hybrid neural network and fuzzy logic approach has been considered to develop a maintainability model using ten different object-oriented static source code metrics as input. This method is applied on maintainability data of two commercial software products such as UIMS and QUES. Rough set analysis (RSA) and principal component analysis (PCA) are used to select suitable set of metrics from the ten metrics employed to improve performance of maintainability prediction model. From experimental results, it is observed that Neuro-Fuzzy model can effectively predict the maintainability of object-oriented software systems. After implementing parallel computing concept, it is observed that the training time gets reduced to a significant amount when the number of computing nodes were increased. Further it is observed that selected subset of metrics using feature selection techniques i.e., PCA, and RSA was able to predict maintainability with higher accuracy.

Hybrid functional link artificial neural network approach for predicting maintainability of object-oriented software

In present day, software development methodology is mostly based on object-oriented paradigm. With the increase in the number of these software system, their effective maintenance aspects becomes a crucial factor. Most of the maintainability prediction models in literature are based on techniques such as regression analysis and simple neural network. In this paper, three artificial intelligence techniques (AI) such as hybrid approach of functional link artificial neural network (FLANN) with genetic algorithm (GA), particle swarm optimization (PSO) and clonal selection algorithm (CSA), i.e., FLANN-Genetic (FGA and AFGA), FLANN-PSO (FPSO and MFPSO), FLANN-CSA (FCSA) are applied to design a model for predicting maintainability. These three AI techniques are applied to predict maintainability on two case studies such as Quality Evaluation System (QUES) and User Interface System (UIMS). This paper also focuses on the effectiveness of feature reduction techniques such as rough set analysis (RSA) and principal component analysis (PCA) when they are applied for predicting maintainability. The results show that feature reduction techniques are very effective in obtaining better results while using FLANN-Genetic.

STATISTICAL COMPARISON OF MODELLING METHODS FOR SOFTWARE MAINTAINABILITY PREDICTION

The objective of this paper is statistical comparison of modelling methods for software maintainability prediction. The statistical comparison is performed by building software maintainability prediction models using 27 different regression and machine learning based algorithms. For this purpose, software metrics datasets of two different commercial object-oriented systems are used. These systems were developed using an object oriented programming language Ada. These systems are User Interface Management System (UIMS) and Quality Evaluation System (QUES). It is shown that different measures like MMRE, RMSE, Pred(0.25) and Pred(0.30) calculated on predicted values obtained from leave one out (LOO) cross validation produce very divergent results regarding accuracy of modelling methods. Therefore the 27 modelling methods are evaluated on the basis of statistical significance tests. The Friedman test is used to rank various modelling methods in terms of absolute residual error. Six out of the ten top ranked modelling methods are common to both UIMS and QUES. This indicates that modelling methods for software maintainability predicton are solid and scalable. After obtaining ranks, pair wise Wilcoxon Signed rank test is performed. Wilcoxon Sign rank test indicates that the top ranking method in UIMS data set is significantly superior to only four other modelling methods whereas the top tanking method in QUES data set is significantly superior to 11 other modelling methods. The performance of instance based learning algorithms — IBk and Kstar is comparable to modelling methods used in earlier studies.

MAINTAINABILITY PREDICTORS FOR RELATIONAL DATABASE-DRIVEN SOFTWARE APPLICATIONS: EXTENDED RESULTS FROM A SURVEY

Software maintainability is a very important quality attribute. Its prediction for relational database-driven software applications can help organizations improve the maintainability of these applications. The research presented herein adopts a survey-based approach where a survey was conducted with 40 software professionals aimed at identifying and ranking the important maintainability predictors for relational database-driven software applications. The survey results were analyzed using frequency analysis. The results suggest that maintainability prediction for relational database-driven applications is not the same as that of traditional software applications in terms of the importance of the predictors used for this purpose. The results also provide a baseline for creating maintainability prediction models for relational database-driven software applications.

Building measure-based prediction models for UML class diagram maintainability

The usefulness of measures for the analysis and design of object oriented (OO) software is increasingly being recognized in the field of software engineering research. In particular, recognition of...

Predicting object-oriented software maintainability using multivariate adaptive regression splines

Accurate software metrics-based maintainability prediction can not only enable developers to better identify the determinants of software quality and thus help them improve design or coding, it can also provide managers with useful information to help them plan the use of valuable resources. In this paper, we employ a novel exploratory modeling technique, multiple adaptive regression splines (MARS), to build software maintainability prediction models using the metric data collected from two different object-oriented systems. The prediction accuracy of the MARS models are evaluated and compared using multivariate linear regression models, artificial neural network models, regression tree models, and support vector models. The results suggest that for one system MARS can predict maintainability more accurately than the other four typical modeling techniques, and that for the other system MARS is as accurate as the best modeling technique.

An application of Bayesian network for predicting object-oriented software maintainability

As the number of object-oriented software systems increases, it becomes more important for organizations to maintain those systems effectively. However, currently only a small number of maintainability prediction models are available for object-oriented systems. This paper presents a Bayesian network maintainability prediction model for an object-oriented software system. The model is constructed using object-oriented metric data in Li and Henry's datasets, which were collected from two different object-oriented systems. Prediction accuracy of the model is evaluated and compared with commonly used regression-based models. The results suggest that the Bayesian network model can predict maintainability more accurately than the regression-based models for one system, and almost as accurately as the best regression-based model for the other system.