The main reason behind failure of lots of software is poor quality thus estimation software quality become an important task in software industry. An early estimation towards pre-released software quality plays an important role in increasing probability of project success. By analyzing the metric data we can forecast the quality of the object oriented system. Software metrics are necessary to identify where the resources are needed; they are a crucial source of information for decision making. This paper represents proposed model for estimation quality of software product.

A hierarchical model for object-oriented design quality assessment

Code smells and refactoring: A tertiary systematic review of challenges and observations

Refactoring is the process of changing the internal structure of software to improve its quality without modifying its external behavior. Empirical studies have repeatedly shown that refactoring has a positive impact on the understandability and maintainability of software systems. However, before carrying out refactoring activities, developers need to identify refactoring opportunities. Currently, refactoring opportunity identification heavily relies on developers&#x2019; expertise and intuition. In this paper, we investigate the effectiveness of machine learning algorithms in predicting software refactorings. More specifically, we train six different machine learning algorithms (i.e., Logistic Regression, Naive Bayes, Support Vector Machine, Decision Trees, Random Forest, and Neural Network) with a dataset comprising over two million refactorings from 11,149 real-world projects from the Apache, F-Droid, and GitHub ecosystems. The resulting models predict 20 different refactorings at class, method, and variable-levels with an accuracy often higher than 90 percent. Our results show that (i) Random Forests are the best models for predicting software refactoring, (ii) process and ownership metrics seem to play a crucial role in the creation of better models, and (iii) models generalize well in different contexts. 

https://repository.tudelft.nl/islandora/object/uuid%3Afa8152bf-5d7d-408b-8e45-b611af7862fc/datastream/OBJ/download

The Effectiveness of Supervised Machine Learning Algorithms in Predicting Software Refactoring

How does object-oriented code refactoring influence software quality? Research landscape and challenges

This survey reviews published materials related to the specific area of Search-Based Software Engineering that concerns software maintenance and, in particular, refactoring The survey aims to give a comprehensive review of the use of search-based refactoring to maintain software Fifty different papers have been selected from online databases to analyze and review the use of search-based refactoring in software engineering The current state of the research is analyzed and patterns in the studies are investigated in order to assess gaps in the area and suggest opportunities for future research The papers reviewed are tabulated in order to aid researchers in quickly referencing studies The literature addresses different methods using search-based refactoring for software maintenance, as well as studies that investigate the optimization process and discuss components of the search There are studies that analyze different software metrics, experiment with multi-objective techniques and propose refactoring tools for use Analysis of the literature has indicated some opportunities for future research in the area More experimentation of the techniques in an industrial environment and feedback from software developers is needed to support the approaches Also, recent work with multi-objective techniques has shown that there are exciting possibilities for future research using these techniques with refactoring This survey is beneficial as an introduction for any researchers aiming to work in the area of Search-Based Software Engineering with respect to software maintenance and will allow them to gain an understanding of the current landscape of the research and the insights gathered

/pdf/a-survey-of-search-based-refactoring-for-software-2n0m00syd6.pdf

A Survey of Search-Based Refactoring for Software Maintenance

Context Software maintenance is expensive and so anything that can be done to reduce its cost is potentially of huge benefit. However, it is recognised that some maintenance, especially refactoring, can be automated. Given the number of possible refactorings and combinations of refactorings, a search-based approach may provide the means to optimise refactorings. Objective This paper describes the investigation of a many-objective genetic algorithm used to automate software refactoring, implemented as a Java tool, MultiRefactor. Method The approach and tool is evaluated using a set of open source Java programs. The tool contains four separate measures of software looking at the software quality as well as measures of code priority, refactoring coverage and element recentness. The many-objective algorithm combines the four objectives to improve the software in a holistic manner. An experiment has been constructed to compare the many-objective approach against a mono-objective approach that only uses a single objective to measure software quality. Different permutations of the objectives are also tested and compared to see how well the different objectives can work together in a multi-objective refactoring approach. The eight approaches are tested on six different open source Java programs. Results The many-objective approach is found to give better objective scores on average than the mono-objective approach and in less time. However, the priority and element recentness objectives are both found to be less successful in multi/many-objective setups when they are used together. Conclusion A many-objective approach is suitable and effective for optimising automated refactoring to improve quality. Including other objectives does not unduly degrade the quality improvements, but is less effective for those objectives than if they were used in a mono-objective approach.

/pdf/using-a-many-objective-approach-to-investigate-automated-1w3ez3ut8n.pdf

Using a Many-Objective Approach to Investigate Automated Refactoring

ion Coupling Inheritance Technical Debt Fi tn e ss F u n ct io n Im p ro ve m e n t JSON JFlex XML-RPC Mango Beaver JHotDraw

Automated software maintenance using search-based refactoring

This paper proposes a multi-objective genetic algorithm to automate software refactoring and validates the approach using a tool, MultiRefactor, and a set of open source Java programs. The tool uses a metric function to measure quality in a software system and tests a second objective to measure the recentness of the code elements being refactored. Previous versions of the software project are analyzed and a recentness measure is then calculated with respect to previous versions of code. The multi-objective setup refactors the input program to improve its quality using the quality objective, while also focusing on the recentness of the code elements inspected. An experiment has been constructed to measure the multi-objective approach against an alternative mono-objective approach that does not use an objective to measure element recentness. The two approaches are validated using six different open source Java programs. The multi-objective approach is found to give significantly better recentness scores across all inputs in a similar time, while also generating improvements in the quality score.

/pdf/automated-refactoring-of-software-using-version-history-and-53h6ncj2rm.pdf

Automated Refactoring of Software Using Version History and a Code Element Recentness Measure

This paper describes a multi-objective genetic algorithm used to automate software refactoring. The approach is validated using a set of open source Java programs with a purpose built tool, MultiRefactor. The tool uses a metric function to measure quality in a software system and tests a second objective to measure the amount of code coverage of the applied refactorings by analyzing the code elements they have been applied to. The multi-objective setup will refactor the input program to improve its quality using the quality objective, while also maximizing the code coverage of the refactorings applied to the software. An experiment has been constructed to measure the multi-objective approach against the alternative mono-objective approach that does not use an objective to measure refactoring coverage. The two approaches are tested on six different open source Java programs. The multi-objective approach is found to give significantly better refactoring coverage scores across all inputs in a similar time, while also generating improvements in the quality scores.

/pdf/maximizing-refactoring-coverage-in-an-automated-maintenance-11eh59sc43.pdf

Maximizing refactoring coverage in an automated maintenance approach using multi-objective optimization

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