Grammatical Evolution for the Multi-Objective Integration and Test Order Problem

Search techniques have been successfully applied for solving different software testing problems. However, choosing, implementing and configuring a search technique can be hard tasks. To reduce efforts spent in such tasks, this paper presents an offline hyper-heuristic named GEMOITO, based on Grammatical Evolution (GE). The goal is to automatically generate a Multi-Objective Evolutionary Algorithm (MOEA) to solve the Integration and Test Order (ITO) problem. The MOEAs are distinguished by components and parameters values, described by a grammar. The proposed hyper-heuristic is compared to conventional MOEAs and to a selection hyper-heuristic used in related work. Results show that GEMOITO can generate MOEAs that are statistically better or equivalent to the compared algorithms

Metaheuristic Design Pattern: Visitor for Genetic Operators

Metaheuristics, such as Genetic Algorithms (GAs), and hyper-heuristics have been widely studied and applied in the literature. This led to the development of several frameworks to aid the execution and development of such algorithms. Consequently, the reusability, scalability and maintainability became fundamental points to be attacked by developers. Such points can be improved using Design Patterns, but despite their advantages, few works have explored their usage with metaheuristics and hyper-heuristics. In order to contribute to this research topic, we present a solution based on the Visitor pattern used to design genetic operators. A case study is presented with the Hyper-heuristic for the Integration and Test Order problem (HITO). This case study shows that the proposed solution can increase the reusability of the implemented operators, and also enable easy addition of new genetic operators and representations

An Approach for the Generation of Multi-Objective Algorithms Applied to the Integration and Test Order Problem

Multi-Objective Evolutionary Algorithms (MOEAs) have been successfully applied to solve hard real software engineering problems. However, to choose and design a MOEA is considered a difficult task, since there are several parameters and components to be configured. These aspects directly impact the generated solutions and the performance of MOEAs. In this sense, this paper proposes an approach for the automatic generation of MOEAs applied to the Integration and Test Order (ITO) problem. Such a problem refers to the generation of optimal sequences of units for integration testing. The approach includes a set of parameters and components of different MOEAs, and is implemented with two design algorithms: Grammatical Evolution (GE) and Iterated Racing (irace). Evaluation results are presented, comparing the MOEAs generated by both design algorithms. Furthermore, the generated MOEAs are compared to two well-known MOEAs used in the literature to solve the ITO problem. Results show that the MOEAs generated with GE and irace perform similarly, and both outperform traditional MOEAs. The approach can reduce efforts spent to design and configure MOEAs, and serves as basis for implementing solutions to other software engineering problems

A pattern-driven solution for designing multi-objective evolutionary algorithms

Multi-objective evolutionary algorithms (MOEAs) have been widely studied in the literature, which led to the development of several frameworks and techniques to implement them. Consequently, the reusability, scalability and maintainability became fundamental concerns in the development of such algorithms. To this end, the use of design patterns (DPs) can benefit, ease and improve the design of MOEAs. DPs are reusable solutions for common design problems, which can be applied to almost any context. Despite their advantages to decrease coupling, increase flexibility, and allow an easier design extension, DPs have been underexplored for MOEA design. In order to contribute to this research topic, we propose a pattern-driven solution for the design of MOEAs. The MOEA designed with our solution is compared to another MOEA designed without it. The comparison considered: the Integration and Test Order (ITO) problem and the Traveling Salesman problem (TSP). Obtained results show that the use of this DP-driven solution allows the reuse of MOEA components, without decreasing the quality, in terms of hypervolume. This means that the developer can extend the algorithms to include other components using only object-oriented mechanisms in an easier way, while maintaining the expected results

The Symposium on Search-Based Software Engineering: Past, Present and Future

CONTEXT: Search-Based Software Engineering (SBSE) is the research field where Software Engineering (SE) problems are modelled as search problems to be solved by search-based techniques. The Symposium on Search Based Software Engineering (SSBSE) is the premier event on SBSE, which had its 11th edition in 2019. OBJECTIVE: In order to better understand the characteristics and evolution of papers published at SSBSE, this work reports results from a mapping study targeting the proceedings of all SSBSE editions. Despite the existing mapping studies on SBSE, our contribution in this work is to provide information to researchers and practitioners willing to enter the SBSE field, being a source of information to strengthen the symposium, guide new studies, and motivate new collaboration among research groups. METHOD: A systematic mapping study was conducted with a set of four research questions, in which 134 studies published in all editions of SSBSE, dated from 2009 to 2019, were evaluated. In a fifth question, 32 papers published in the challenge track were summarised. RESULTS: Throughout the years, 290 authors from 25 countries have contributed to the main track of the symposium, with the collaboration of at least two institutions in 46.3% of the papers. SSBSE papers have got substantial external visibility, as most citations are from different venues. The SE tasks addressed by SSBSE are mostly related to software testing, software debugging, software design, and maintenance. Evolutionary algorithms are present in 75% of the papers, being the most common search technique. The evaluation of the SBSE approaches usually includes industrial systems. CONCLUSIONS: SSBSE has helped increase the popularity of SBSE in the SE research community and has played an important role on making SBSE mature. There are still problems and challenges to be addressed in the SBSE field, which can be tackled by SSBSE authors in further studies

A multi-objective and evolutionary hyper-heuristic applied to the Integration and Test Order Problem

The field of Search-Based Software Engineering (SBSE) has widely utilized Multi-Objective Evolutionary Algorithms (MOEAs) to solve complex software engineering problems. However, the use of such algorithms can be a hard task for the software engineer, mainly due to the significant range of parameter and algorithm choices. To help in this task, the use of Hyper-heuristics is recommended. Hyper-heuristics can select or generate low-level heuristics while optimization algorithms are executed, and thus can be generically applied. Despite their benefits, we find only a few works using hyper-heuristics in the SBSE field. Considering this fact, we describe HITO, a Hyper-heuristic for the Integration and Test Order Problem, to adaptively select search operators while MOEAs are executed using one of the selection methods: Choice Function and Multi-Armed Bandit. The experimental results show that HITO can outperform the traditional MOEAs NSGA-II and MOEA/DD. HITO is also a generic algorithm, since the user does not need to select crossover and mutation operators, nor adjust their parameters

Constraint based structural testing criteria

Structural criteria generally divide the input domain of the program under test and require the execution of at least one point from each derived sub-domain without addressing the most relevant question: "Which points from each sub-domain should be selected?". This question is related to data-sensitive faults which lead to one of the drawbacks of the testing activity. The constraints and conditions used by fault-based data generation techniques describe faults related to the boundaries of these sub-domains. Our conjecture is that we would improve the efficacy of the adequate test case sets by associating those constraints and conditions to the elements required by a criterion. With this goal, this work presents Constraint Based Criteria (CBC) that associate a constraint C to an element E, required by a structural criterion. CBC allow to combine the fundamentals of different testing generation techniques with structural testing, increasing the probability of revealing faults described by C. We also discuss complexity, inclusion relation and automation aspects of CBC. Results from three experiments of CBC evaluation using the factors cost, efficacy and strength provide evidence that our objectives have been achieved. We also present some results from the evaluation of random test sets. (C) 2005 Elsevier Inc. All rights reserved.79675677