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

Fuzzy based component reusability evaluation approach to support component based software development

One of the contributions of Component Based Software Development (CBSD) is the reuse of software components across multiple systems by software developers. However, the developers often face a difficulty to determine the reusability of the components during the component selection process. Similarly, the component developers also have a problem to measure the component reusability during component development. Nowadays, even though many studies have been conducted in this field, which the researchers suggested many approaches with metrics but they still lack in empirical confirmation and evidences. Therefore, the aim of this study is to investigate and develop the component reusability evaluation approach to support CBSD. The proposed approach, which is called Component Reusability Evaluation Approach (CREA), is supported by the developed automated tool (CREATool) that may automate the reusability evaluation. CREA is then evaluated by applying five Java component in this approach and CREATool to the selected software components. The results from the application approach and then validated with results from the controlled experiment using statistical analysis. The results indicated that CREA able to provide an acceptable reusability measure, which it is confirmed by similarity results between evaluation using statistical analysis through the controlled experiment and by applying the CREATool. It shows that the proposed approach could be used as an alternative approach in component reusability evaluation. Although the developed approach are not intended to make a holistic and an ultimate decision whether the components can be reused or not, but it is useful enough to be considered as a guide for both component users and developers in making decisions related to reusable components

Predicting Class Life Cycle Maintenance Effort Based on Class Complexity

In the software industry today many programmers spend countless hours maintaining existing Java programs. The cost of code maintenance affects a company in many ways such as the budget, time management and resources. Making management decisions regarding these issues could be assisted, if maintenance cost of Java classes could be predicted. The goal of this thesis was to create a new model predicting the maintenance effort based on the Java class complexity. It seems clear the complexity of a Java class can directly relate to the amount of time it will take to perform maintenance on the class. To develop the new maintenance effort model, a test bed of Java classes was assembled representing a sample of Java classes from the workplace. Then a variety of Java class metrics were calculated using these classes. Using the backward elimination process of regression analysis in SPSS, a new model was created predicting maintenance effort. The metrics that best predicted maintenance effort were the depth of an inheritance tree, the number of times a class has been deployed to the customer and the lines of code. Together, these metrics together were able to predict 85% of the maintenance effort on the set of Java classes tested