Stability Aware Software Refactoring Using Hybrid Search Based Techniques

SUMMARYThis paper proposes novel artificial bee colony (ABC) algorithms for solving problems including interdependence among variables. ABC algorithms are one method of solving multivariable real number space optimization problems, in which the search space is a set of vectors constructed of variables. The main search process in the ordinary ABC algorithm creates a new solution vector by changing only one variable of the current solution vector. Therefore, the new solution vector is created along only one coordinate axis. This procedure, however, is not appropriate for solving problems including interdependence among variables. For such problems, a method that is able to change more than one variable of a solution vector at the same time is required. In our proposed methods, the original coordinate axes are transformed to linearly uncorrelated axes by using principal component analysis (PCA) in the searching process. Our ABC algorithms create a new solution vector along one of the axes transformed by PCA. Hence, from the viewpoint of the original coordinate axes, the new algorithms are able to change more than one variable. The proposed algorithms have been compared with the ordinary ABC algorithm by solving five benchmark problems. Through the computer simulation results, our algorithms were shown to have better performance for solving problems including interdependence among variables than the ordinary ABC algorithm.

Archive-based multi-criteria Artificial Bee Colony algorithm for whole test suite generation

A polygonal approximation method based on the new artificial bee colony (NABC) algorithm is proposed in this paper. In the present method, a solution is represented by a vector, and the objective function is defined as the integral square error between the given curve and its corresponding polygon. The search process, including the employed bee stage, the onlooker bee stage, and the scout bee stage, has been constructed for this specific problem. Most experiments show that the present method when compared with the DE-based method can obtain superior approximation results with less error norm with respect to the original curves.

One of the main goals of software engineering is to assess the quality of the software we develop, however this is to be measured. A software metric, assigning a number to each piece of program text, will generally attempt to address one or more quality attributes in its assessment. These might include software reliability, testability, maintainability, and the like, and, in general, a kind of complexity of the software in some specific and restricted sense. In this tutorial a new cubic flowgraph model is introduced and shown to be useful in exploring a whole range of software metric design and analysis techniques, specifically in reference to the subclass of software metrics that are said to be hierarchical. In particular, the cubic flowgraph model leads to the discovery of certain inequalities among existing metrics, serves to provide a new characterization of the important class of prime flowgraphs, and points the way to an. effective method for counting and enumerating the primes.

Refactoring is a modification in the internal structure of software, in order to improve quality, understandability and maintainability, without changing its observable behavior. Search Based Software Refactoring (SBSR) deals with automatic software refactoring processes using optimization algorithms. In this context, here we investigate the problem of finding a sequence of refactorings that provides code improvement, according to software quality attributes, expressed by a combination of software metrics. There are multiple criteria to define the quality of a solution, therefore this problem is defined as a Many-Objective Combinatorial Optimization Problem. There is a lack of works that focus on Many-Objective Discrete Problems in SBSR. In this direction, this work proposes a Many-Objective Estimation Distributed Algorithm to find a sequence of refactorings on an object-oriented software. The algorithm explores archiving methods and probabilistic models. A set of experiments is performed, with the aim of investigating which is the best algorithm configuration, regarding the probabilistic model and selection procedure.

This paper proposes a new approach for edge detection using a combination of artificial bee colony (ABC) algorithm and an improved derivative technique. ABC algorithm simulates the foraging behavior of a honey bee swarm. The proposed approach find the edge pixels by an improved derivative technique to compute fitness function, and then applying ABC algorithm determine the most fit pixels to be considered as the edge pixels. Qualitative and quantitative analysis of the proposed approach and its comparison with other standard edge detection methods are presented. Shannon’s entropy function and Pratt’s figure of merit are used for quantitative analysis. The effect of variation of parameters on performance of the proposed approach is discussed. Experimental results show that proposed method outperformed most of existing techniques.

Software Refactoring, a critical skill in software development, involves reorganizing code without altering its functionality. Despite its importance, many developers find refactoring complex and risky, often hesitating to adopt tools designed to assist in this process. To bridge this gap in this paper, we investigate the impact of gamification on the refactoring process and the usability of existing refactoring tools. Our research introduces RefGame, an innovative game-based tool that aims to enhance the learning experience in identifying and applying refactoring techniques. We used RefGame in an exploratory study that involved 322 computer science students. Then, we collected the feedback provided by these students via a survey. Our findings provide insight into the potential of gamification to make software refactoring education more accessible and effective, addressing the current challenges in teaching and learning refactoring techniques.

Image segmentation means that the image is divided into specific and unique regions. There are many existing image segmentation methods, and the threshold-based segmentation method is widely applied because of its easy implementation, simplicity and high efficiency. In this paper, artificial bee colony algorithm is applied to image threshold segmentation. Kapur entropy is used as a fitness function, the artificial bee colony algorithm is improved through the adaptive scaling factor. The search area is enlarged through the large-scale factor, the neighborhood search scope is reduced through the small-scale factor and the search efficiency is enhanced. Finally, by comparing the PSNR values of the image, the algorithm has a good segmentation effect and good convergence performance.

In order to make in-depth research on national costume patterns and better inherit and carry forward national culture, the author uses artificial bee colony algorithm to optimize feature extraction and effectively classify clothing patterns. First, the clothing structure database and the noisy features of the clothing structure database are introduced, and the artificial bee colony algorithm is used to classify the target and background in the image with one-dimensional information entropy based on fitness function; After the clothing pattern scanning is input into the computer, the object and background are effectively segmented by using the image threshold segmentation technology, and then stored in the computer in the form of vectorization or for the reuse of clothing pattern images. This kind of storage and application has a positive significance for the national traditional culture and improving the efficiency of dress design for technicians in the national culture.

The Improved Artificial Bee Colony (IABC) algorithm is a variant of the well-known Artificial Bee Colony (ABC) algorithm. In IABC, a new initialization approach and a new search mechanism were added to the ABC for avoiding local optimums and a better convergence speed. New parameters were added for the new search mechanism. Specified values of these newly added parameters have a direct impact on the performance of the IABC algorithm. For better performance of the algorithm, parameter values should be subjected to change from problem to problem and also need to be updated during the run of the algorithm. In this paper, two novel parameter control methods and related algorithms have been developed in order to increase the performance of the IABC algorithm for large scale optimization problems. One of them is an adaptive parameter control which updates parameter values according to the feedback coming from the search process during the run of the algorithm. In the second method, the management of the parameter values is left to the algorithm itself, which is called self-adaptive parameter control. The adaptive IABC algorithms were examined and compared to other ABC variants and state-of-the-art algorithms on a benchmark functions suite. Through the analysis of the results of the experiments, the adaptive IABC algorithms outperformed almost all ABC variants and gave competitive results with state-of-the-art algorithms from the literature.

Accurate segmentation of retinal blood vessels is an important task in computer aided diagnosis of retinopathy. In this paper, we propose an automated retinal blood vessel segmentation approach based on artificial bee colony optimisation in conjunction with fuzzy c-means clustering. Artificial bee colony optimisation is applied as a global search method to find cluster centers of the fuzzy c-means objective function. Vessels with small diameters appear distorted and hence cannot be correctly segmented at the first segmentation level due to confusion with nearby pixels. We employ a pattern search approach to optimisation in order to localise small vessels with a different fitness function. The proposed algorithm is tested on the publicly available DRIVE and STARE retinal image databases and confirmed to deliver performance that is comparable with state-of-the-art techniques in terms of accuracy, sensitivity and specificity.

A new topology optimization scheme based on a Harmony search (HS) as a metaheuristic method was proposed and applied to static stiffness topology optimization problems. To apply the HS to topology optimization, the variables in HS were transformed to those in topology optimization. Compliance was used as an objective function, and harmony memory was defined as the set of the optimized topology. Also, a parametric study for Harmony memory considering rate (HMCR), Pitch adjusting rate (PAR), and Bandwidth (BW) was performed to find the appropriate range for topology optimization. Various techniques were employed such as a filtering scheme, simple average scheme and harmony rate. To provide a robust optimized topology, the concept of the harmony rate update rule was also implemented. Numerical examples are provided to verify the effectiveness of the HS by comparing the optimal layouts of the HS with those of Bidirectional evolutionary structural optimization (BESO) and Artificial bee colony algorithm (ABCA). The following conclusions could be made: (1) The proposed topology scheme is very effective for static stiffness topology optimization problems in terms of stability, robustness and convergence rate. (2) The suggested method provides a symmetric optimized topology despite the fact that the HS is a stochastic method like the ABCA. (3) The proposed scheme is applicable and practical in manufacturing since it produces a solid-void design of the optimized topology. (4) The suggested method appears to be very effective for large scale problems like topology optimization.

Software refactoring is the process of reorganizing the internal structure of code while preserving the external behavior. Aspect-Oriented Programming (AOP) provides new modularization of software systems by encapsulating crosscutting concerns. Based on these two techniques, aspect-oriented (AO) refactoring restructures crosscutting elements in code. AO refactoring includes two steps: aspect mining (identification of aspect candidates in code) and aspect refactoring (semantic-preserving transformation to migrate the aspect-candidate code to AO code). Aspect refactoring clusters similar join points together for the aspect candidates and encapsulates each cluster with an effective pointcut definition. With the increase in size of the code and crosscutting concerns, it is tedious to manually identify aspects and their corresponding join points, cluster the join points, and infer pointcut expressions. Therefore, there is a need to automate the process of AO refactoring. This paper proposes an automated approach that identifies aspect candidates in code and infers pointcut expressions for these aspects. Our approach mines for aspect candidates, identifies the join points for the aspect candidates, clusters the join points, and infers an effective pointcut expression for each cluster of join points. The approach also provides an additional testing mechanism to ensure that the inferred pointcut expressions are of correct strength. The empirical results show that our approach helps achieve a significant reduction in the total number of pointcut expressions to be used in the refactored code.

One of the established trends of research areas and practices in software engineering that dealt with the measurement and enhancement of reliability is software reliability engineering. Stochastic software reliability models find typical usage for analysis. These are the models that perform modeling on failure process of the software and exploit other software metrics or failure data as cornerstone for parameter estimation. The ability of the models for estimating and predicting the current reliability and future failure behavior, respectively, is high. Due to any failures and faults in the system, a product becomes unreliable. The lack of understanding of nature of the software makes the measurement of software reliability as a challenging task. It is not possible to determine a best way to measure the reliability and other aspects of software. This paper proposes an efficient software reliability growth model (SRGM) in which logistic exponential TEF is exploited. This model offers increased failure rate recognition and suitable ways to resolve faults and so on. Our work estimates the SRGM parameters using optimization algorithm. Such estimation can aid in developing precise software reliability model. In order to accomplish the optimization, we use artificial bee colony (ABC) algorithm. As the parameters optimization considerably improves the quality of parameters to be used for reliability growth model, reliability growth can also be improved considerably.

Energy storage systems are acknowledged as key components for transforming the power system towards low-carbon technology. The performance and resulting benefit of energy storage systems are determined by their operation strategies. We present a generic and adaptive control algorithm, based on fuzzy-logic and optimized by a meta heuristic search method. The performance of the algorithm is demonstrated in a solar home context, with the aim to alleviate the voltage rise in the low-voltage distribution grid caused by renewable energy generation. This is achieved by reducing the household's peak feed-in of each day. The obtained results show that the proposed algorithm performs similarly well as a rule-based reference algorithm, specifically designed to reduce the daily feed-in peak. At the same time, the generic structure of the proposed controller is by far more versatile, as it allows its application in a variety of scenarios just by modifying the objective function for the search algorithm.