A Many-Objective Estimation Distributed Algorithm Applied to Search Based Software Refactoring

Using reference points to update the archive of MOPSO algorithms in Many-Objective Optimization

Many-objective optimization problems are problems that have more than three objective functions. Traditional multi-objective evolutionary algorithms scale poorly when the number of objective functions increases. Recently, different approaches have been proposed for improving the performance of these algorithms on many-objective optimization problems. One of these approaches is the use of multiple populations on multi-objective particle swarm optimization, called multi-swarm. Multi-swarm techniques explore parallel populations to decompose the problem and optimize them in a collaborative manner. This paper presents a new multi-swarm algorithm, called Multi-Swarm Algorithm Based on Archiving and Topologies (MSAT). MSAT combines different archiving methods and communication topologies aiming to obtain good convergence and diversity in many-objective optimization problems. An experimental set is performed seeking to find the best combination of archiving and topology methods. Furthermore, MSAT is confronted to NSGA-III algorithm in different scenarios. MSAT outperformed NSGA-III both in terms of convergence and diversity, for concave and convex problems.

Software metrics is a necessary step for software reliability and quality, and software metrics of traditional procedure-oriented programming are fairly mature and have various methodologies and tools available for use. Object-oriented programming has recently became popular. However, traditional procedure-oriented software metrics are not appropriate for the development of object-oriented software. Some research into object-oriented software metrics has been proposed, but these articles focus on only one metric that measures a specific characteristic of the object-oriented software. The authors propose a new metric methodology, the data scope complexity, for object-oriented software based on the data scope of a program. The data scope complexity can show complexities of multiple features of object-oriented programming at the same time. They also quantify and compare object-oriented programming with procedure-oriented programming.

Object-oriented software metrics are important in software engineering field for measurement of the software quality, size estimation, complexity and effort. Object oriented software development is the most popular and best practice in software development. Object oriented software metrics can be used for measuring extend of object orientedness of a program. These quantities are important information indicators, that can be helpful for a software developer in take decisions for designing good software. It is proven that the software metrics provide quantitative information about the structure of the code, which can be used to determine the reliability of software. The design of the software is highly recommended to be object oriented in the software development environment. Software metrics specific to programs that have object oriented design can be used to measure the quality of the software like inheritance, assessing the effects of coupling, the complexity of the code, size aspects, etc, of the software developed in such an environment. Software measurement is a way for assessing the quality of software. Many attributes can define quality of a software, internal (size, complexity) as well as external (reliability)[14]. Often internal attributes are contributing factors to external attributes. Reliability as a term means the degree to which a component, product or system executes specified functions for a specific period of time under specified conditions. Software reliability is the software being trustworthy of the specifications. The aim of the work is to model the relationship between reliability of object oriented software to the object oriented design metrics and predict reliability of software.

Tensor decomposition-based alternate sub-population evolution for large-scale many-objective optimization

A reference direction and entropy based evolutionary algorithm for many-objective optimization

There is no software metrics based on object-oriented programming languages (OOPLs) developed to help object-oriented software development. A graph-theoretical complexity metric to measure object-oriented software complexity is described. It shows that inheritance has a close relation with the object-oriented software complexity, and reveals that misuse of repeated (multiple) inheritance will increase software complexity and be prone to implicit software errors. An algorithm to support this software metric is presented. Its time complexity is O(n/sup 3/). >

Code smells are generally not considered as bugs; instead, they point out certain shortcomings in the software design or code. Identification of code smell is a necessary step for improving the software quality and reducing the maintenance effort. In this study, we introduce a bad smell prediction technique based on object-oriented software metrics that use Decision Tree (DT) and Random Forest (RF) machine learning algorithm. An open-source project, namely JHOTDRAW, was used as our dataset, for which values of object-oriented software metrics were calculated. Two feature selection methods-Random Forest Importance (RFI) and Information Gain (IG) were applied to extract the most relevant attributes for the prediction of code smells, namely, Feature envy, Dispersed coupling, refused parent bequest, and God class. The random-search algorithm was used to tune the parameters of Random Forest and Decision Tree. Results show that the best classification accuracy for Decision Tree was obtained at 99.13% for refused parent bequest code smells. Results also show that after using the Random Forest classifier, refused parent bequest smell was predicted with the best accuracy of 99.14%. Finally, in this research study, a set of code smell prediction rules were extracted using Decision Tree.

We propose a surrogate-assisted reference vector adaptation (SRVA) method to\nsolve expensive multi- and many-objective optimization problems with various\nPareto front shapes. SRVA is coupled with a multi-objective Bayesian\noptimization (MBO) algorithm using reference vectors for scalarization of\nobjective functions. The Kriging surrogate models for MBO is used to estimate\nthe Pareto front shape and generate adaptive reference vectors uniformly\ndistributed on the estimated Pareto front. We combine SRVA with expected\nimprovement of penalty-based boundary intersection as an infill criterion for\nMBO. The proposed algorithm is compared with two other MBO algorithms by\napplying them to benchmark problems with various Pareto front shapes.\nExperimental results show that the proposed algorithm outperforms the other two\nin the problems whose objective functions are reasonably approximated by the\nKriging models. SRVA improves diversity of non-dominated solutions for these\nproblems with continuous, discontinuous, and degenerated Pareto fronts.\nBesides, the proposed algorithm obtains much better solutions from early stages\nof optimization especially in many-objective problems.\n

Evolutionary many-objective optimization: A quick-start guide

Software refactoring is a crucial and necessary activity for the quality enhancement during maintenance and evolution of a software system. Stability is a key quality aspect that needs to be monitored especially for the process of refactoring. Due to the nature of the larger possible options for refactoring, search based software engineering practices provide a best possible way in improving the code quality through refactoring. A combination of automated refactoring techniques and metaheuristic search methods, along with the aid of software metrics, can improve the structure of the code. In this work, four quality attributes, namely abstraction, coupling, inheritance, along with stability is taken as fitness functions that drive the search process. The calculation of the fitness function follows a weighted aggregate method of the software metrics. A hybrid Gravitational Search Algorithm and Artificial Bee Colony algorithm (GSA-ABC) is applied which provides improved results in the search process of refactoring options. The results show improvement in terms of average quality gain of the four measured quality factors for the refactored software.

many-objective optimization is widely used in engineering area. There are some flaws to deal with many-objective optimization problem which the number of objectives exceeded three. The method which could chose proper individual solution is very crucial to solve high-dimension many-objective optimization problem. A sub-objective evolutionary algorithm( SOEA) was put forward to solve this problem. It was given in an abstract way to get the non-dominance solutions of high-dimension many-objective optimization problem. Firstly,the value of sub-objective function was sorted,and then partial Pareto non-dominance solutions of evolutional set were obtained quickly. By using the information of sorting,it could reduce the times of solution comparison in evolutional set and could get the solutions quickly. A uniform difference Minkowski distance algorithm and "k-neighbor"strategy were applied to compute fitness function. By using this method,it could improve the convergence speed to approach Pareto non-dominance solutions. Compared with the algorithms which can solve many-objective optimization problem for computing standard testing functions,it was showed the better performance of the SOEA algorithm.

As an essential component in multi- and many-objective optimization, decision-making process either selects a subset of solutions from the whole Pareto front or guides the search toward a small part of the Pareto front during the evolutionary process. In recent years, for many-objective optimization problems (MaOPs), a number of evolutionary algorithms have been developed to search for Pareto optimal solutions. However, there is a lack of research works focusing on designing decision-making approaches. In order to overcome this deficiency, we propose a novel knee-based decision-making method to search for several solutions of interest (SOIs) from a large number of solutions on the Pareto front, each of which contains the best convergence performance at least within its neighborhood and can be identified as a global or local knee solution. The optimization performance achieved by all SOIs approximates the performance of the whole Pareto front as much as possible. Furthermore, in order to relieve the difficulties in the decision-making process on MaOPs, a new visualization approach is developed based on this proposed decision-making approach. It provides information about the shape and location of the Pareto front, the possible bulge, as well as the convergence degree and distribution of solutions. The experimental results on several benchmark functions demonstrate the superiority of the proposed design in the selection of SOIs and visualization of high-dimensional objective space.

A multi-population evolutionary algorithm with single-objective guide for many-objective optimization

Software patterns are often considered as an important methodology in the process of analysis and design of a software, when the proposed architecture of the software is a complex one. During the process of reverse engineering, design patterns become helpful to analyze the source code. A pattern-based analysis often improves maintainability of the source code. The proposed reverse engineering approach considers software metrics for the development of the datasets. The study is comprises of two phases such as preparation of requisite dataset based on object-oriented software metrics and identification of design patterns. Preparation of dataset is carried out using pattern instances from source code, mapped with software metrics. Analysis of design patterns is accomplished by considering a learning-based method i.e., Artificial Neural Network. In this study, two open source software such as JRefactory and Quaqua have been considered to exploit the idea of reverse engineering.