A systematic literature review of machine learning techniques for software maintainability prediction

Context: Software maintainability is one of the fundamental quality attributes of software engineering. The accurate prediction of software maintainability is a significant challenge for the effective management of the software maintenance process. Objective: The major aim of this paper is to present a systematic review of studies related to the prediction of maintainability of object-oriented software systems using machine learning techniques. This review identifies and investigates a number of research questions to comprehensively summarize, analyse and discuss various viewpoints concerning software maintainability measurements, metrics, datasets, evaluation measures, individual models and ensemble models. Method: The review uses the standard systematic literature review method applied to the most common computer science digital database libraries from January 1991 to July 2018. Results: We survey 56 relevant studies in 35 journals and 21 conference proceedings. The results indicate that there is relatively little activity in the area of software maintainability prediction compared with other software quality attributes. CHANGE maintenance effort and the maintainability index were the most commonly used software measurements (dependent variables) employed in the selected primary studies, and most made use of class-level product metrics as the independent variables. Several private datasets were used in the selected studies, and there is a growing demand to publish datasets publicly. Most studies focused on regression problems and performed k-fold cross-validation. Individual prediction models were employed in the majority of studies, while ensemble models relatively rarely. Conclusion: Based on the findings obtained in this systematic literature review, ensemble models demonstrated increased accuracy prediction over individual models, and have been shown to be useful models in predicting software maintainability. However, their application is relatively rare and there is a need to apply these, and other models to an extensive variety of datasets with the aim of improving the accuracy and consistency of results

Exploring Maintainability Assurance Research for Service- and Microservice-Based Systems: Directions and Differences

To ensure sustainable software maintenance and evolution, a diverse set of activities and concepts like metrics, change impact analysis, or antipattern detection can be used. Special maintainability assurance techniques have been proposed for service- and microservice-based systems, but it is difficult to get a comprehensive overview of this publication landscape. We therefore conducted a systematic literature review (SLR) to collect and categorize maintainability assurance approaches for service-oriented architecture (SOA) and microservices. Our search strategy led to the selection of 223 primary studies from 2007 to 2018 which we categorized with a threefold taxonomy: a) architectural (SOA, microservices, both), b) methodical (method or contribution of the study), and c) thematic (maintainability assurance subfield). We discuss the distribution among these categories and present different research directions as well as exemplary studies per thematic category. The primary finding of our SLR is that, while very few approaches have been suggested for microservices so far (24 of 223, ?11%), we identified several thematic categories where existing SOA techniques could be adapted for the maintainability assurance of microservices

Predicting and Evaluating Software Model Growth in the Automotive Industry

The size of a software artifact influences the software quality and impacts the development process. In industry, when software size exceeds certain thresholds, memory errors accumulate and development tools might not be able to cope anymore, resulting in a lengthy program start up times, failing builds, or memory problems at unpredictable times. Thus, foreseeing critical growth in software modules meets a high demand in industrial practice. Predicting the time when the size grows to the level where maintenance is needed prevents unexpected efforts and helps to spot problematic artifacts before they become critical. Although the amount of prediction approaches in literature is vast, it is unclear how well they fit with prerequisites and expectations from practice. In this paper, we perform an industrial case study at an automotive manufacturer to explore applicability and usability of prediction approaches in practice. In a first step, we collect the most relevant prediction approaches from literature, including both, approaches using statistics and machine learning. Furthermore, we elicit expectations towards predictions from practitioners using a survey and stakeholder workshops. At the same time, we measure software size of 48 software artifacts by mining four years of revision history, resulting in 4,547 data points. In the last step, we assess the applicability of state-of-the-art prediction approaches using the collected data by systematically analyzing how well they fulfill the practitioners' expectations. Our main contribution is a comparison of commonly used prediction approaches in a real world industrial setting while considering stakeholder expectations. We show that the approaches provide significantly different results regarding prediction accuracy and that the statistical approaches fit our data best

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

Advanced AI technologies are serving humankind in a number of ways, from healthcare to manufacturing. Advanced automated machines are quite expensive, but the end output is supposed to be of the highest possible quality. Depending on the agility of requirements, these automation technologies can change dramatically. The likelihood of making changes to automation software is extremely high, so it must be updated regularly. If maintainability is not taken into account, it will have an impact on the entire system and increase maintenance costs. Many companies use different programming paradigms in developing advanced automated machines based on client requirements. Therefore, it is essential to estimate the maintainability of heterogeneous software. As a result of the lack of widespread consensus on software maintainability prediction (SPM) methodologies, individuals and businesses are left perplexed when it comes to determining the appropriate model for estimating the maintainability of software, which serves as the inspiration for this research. A structured methodology was designed, and the datasets were preprocessed and maintainability index (MI) range was also found for all the datasets expect for UIMS and QUES, the metric CHANGE is used for UIMS and QUES. To remove the uncertainty among the aforementioned techniques, a popular multiple criteria decision-making model, namely the technique for order preference by similarity to ideal solution (TOPSIS), is used in this work. TOPSIS revealed that GARF outperforms the other considered techniques in predicting the maintainability of heterogeneous automated software.Comment: Submitted for peer revie

Predicting code refactoring via analyzing the history of quality metrics and code anti-patterns

Code refactoring is the process of improving the internal structure of existing code without altering its functionality. Refactoring can help to reduce technical debt, enhance the quality of the code and make the code easy to evolve. However, the manual identification of the proper code refactoring operations to apply can be time-consuming and not scalable. In this thesis, we propose an approach based on data mining and machine learning techniques to analyze historical data and predict refactoring operations that may occur in a future release of a project. The approach uses a combination of techniques to identify patterns in the data and make predictions about which refactoring operations should be applied. In this study, we validated the proposed machine learning based approaches with 13 open-source projects with different releases. We identified the refactoring operations and code smells and extracted the quality metrics for each project release. We used the collected data (e.g. quality metrics and code smells) to predict refactoring operations, and we reported the prediction results based on cross- validation procedures. The proposed research contributes to the field of software quality by providing an efficient and effective approach to refactor the code. The findings of this research will also help developers by suggesting appropriate refactoring operations based on the history of the evolution of software projects. This will ultimately result in improved software quality, reduced technical debt, and enhanced software performance