High dimensional search-based software engineering

Abstract

There is a growing need for scalable search-based software engineering approaches that address software engineering problems where a large number of objectives are to be optimized. Software refactoring is one of these problems where a refactoring sequence is sought that optimizes several software metrics. Most of the existing refactoring work uses a large set of quality metrics to evaluate the software design after applying refactoring operations, but current search-based software engineering approaches are limited to using a maximum of five metrics. We propose for the first time a scalable search-based software engineering approach based on a newly proposed evolutionary optimization method NSGA-III where there are 15 different objectives to be optimized. In our approach, automated refactoring solutions are evaluated using a set of 15 distinct quality metrics. We evaluated this approach on seven large open source systems and found that, on average, more than 92% of code smells were corrected. Statistical analysis of our experiments over 31 runs shows that NSGA-III performed significantly better than two other many-objective techniques (IBEA and MOEA/D), a multiobjective algorithm (NSGA-II) and two mono-objective approaches, hence demonstrating that our NSGA-III approach represents the new state of the art in fully-automated refactoring.

Filtered prioritized and cluster-based optimization towards correction of antipatterns

“…The scalability of NSGA III is illustrated in Mkaouer et al. 6 We compared the computational time and IGD for the algorithms such as high-dimensional NSGA III, 6 our adapted NSGA III, and multi-objective NSGA II (MORE). Our aim is to ensure the efficacy of our adapted approach.…”

“…Each algorithm is executed for 31 runs with different configurations and parameters, then the performance parameters such as CCR, ERE, computational time, the average Euclidean distance differentiating each reference point from its closest one that is termed as the IGD (inverted generational distance), and quality gain are calculated for the two algorithms and for each open-source system. The scalability of NSGA III is illustrated in Mkaouer et al 6 We compared the computational time and IGD for the algorithms such as high-dimensional NSGA III, 6 our adapted NSGA III, and multi-objective NSGA II (MORE). Our aim is to ensure the efficacy of our adapted approach.…”

“…Choudhary and Singh 28 proposed an approach to minimize the refactoring based on historical, relevance, and code smell information, but the three-step filtration process filters out much of the relevant classes, and the design quality is not checked. Mkaouer et al 6 proposed the study on the scalability of the NSGA III algorithm for 15 different lowlevel design metrics and claimed as the first study using NSGA III. The approach uses static metric analyzers to detect the antipatterns, and no attempt is made to improve the refactoring effort.…”

“…The first part is well covered in various literatures, 3,5 but all these approaches are not interactive by putting the programmer into loop and purely metric based one. In the study by Mkaouer et al., 6 a high-dimensional search-based scalable approach is recommended, but the search space is still large to handle.…”

FixedSum: An improved algorithm for generation of weight vectors for decomposition-based multiobjective optimization

“…Such problems are commonly called as many-objective optimization problems [1,[19][20][21]. Most computing and software engineering problems identified in various surveys [22][23][24][25] involve optimizing more than five objectives. For instance, the software project portfolio optimization problem [26] involves about eight goals which include maximization of potential revenue, minimizing the available resources, strategic similarity of projects with software development organization, maximization of positive as well as minimization of negative synergy effects with-inthe projects selected for a portfolio.…”