Wesley Klewerton Guez Assunção

A multi-objective optimization approach for the integration and test order problem

A common problem found during the integration testing is to determine an order to integrate and test the units. Important factors related to stubbing costs and constraints regarding to the software development context must be considered. To solve this problem, the most promising results were obtained with multi-objective algorithms, however few algorithms and contexts have been addressed by existing works. Considering such fact, this paper aims at introducing a generic approach based on multi-objective optimization to be applied in different development contexts and with distinct multi-objective algorithms. The approach is instantiated in the object and aspect-oriented contexts, and evaluated with real systems and three algorithms: NSGA-II, SPEA2 and PAES. The algorithms are compared by using different number of objectives and four quality indicators. Results point out that the characteristics of the systems, the instantiation context and the number of objectives influence on the behavior of the algorithms. Although for more complex systems, PAES reaches better results, NSGA-II is more suitable to solve the referred problem in general cases, considering all systems and indicators.

Establishing integration test orders of classes with several coupling measures

During the inter-class test, a common problem, named Class Integration and Test Order (CITO) problem, involves the determination of a test class order that minimizes stub creation effort, and consequently test costs. The approach based on Multi-Objective Evolutionary Algorithms (MOEAs) has achieved promising results because it allows the use of different factors and measures that can affect the stubbing process. Many times these factors are in conflict and usually there is no a single solution for the problem. Existing works on MOEAs present some limitations. The approach was evaluated with only two coupling measures, based on the number of attributes and methods of the stubs to be created. Other MOEAs can be explored and also other coupling measures. Considering this fact, this paper investigates the performance of two evolutionary algorithms: NSGA-II and SPEA2, for the CITO problem with four coupling measures (objectives) related to: attributes, methods, number of distinct return types and distinct parameter types. An experimental study was performed with four real systems developed in Java. The obtained results point out that the MOEAs can be efficiently used to solve this problem with several objectives, achieving solutions with balanced compromise between the measures, and of minimal effort to test.

Controversy Corner: Search Based Software Engineering: Review and analysis of the field in Brazil

Search Based Software Engineering (SBSE) is the field of software engineering research and practice that applies search based techniques to solve different optimization problems from diverse software engineering areas. SBSE approaches allow software engineers to automatically obtain solutions for complex and labor-intensive tasks, contributing to reduce efforts and costs associated to the software development. The SBSE field is growing rapidly in Brazil. The number of published works and research groups has significantly increased in the last three years and a Brazilian SBSE community is emerging. This is mainly due to the Brazilian Workshop on Search Based Software Engineering (WOES), co-located with the Brazilian Symposium on Software Engineering (SBES). Considering these facts, this paper presents results of a mapping we have performed in order to provide an overview of the SBSE field in Brazil. The main goal is to map the Brazilian SBSE community on SBES by identifying the main researchers, focus of the published works, fora and frequency of publications. The paper also introduces SBSE concerns and discusses trends, challenges, and open research problems to this emergent area. We hope the work serves as a reference to this novel field, contributing to disseminate SBSE and to its consolidation in Brazil.

Feature location for software product line migration: a mapping study

Developing software from scratch is a high cost and error-prone activity. A possible solution to reduce time-to-market and produce high quality software is the reuse of existing software. But when the number of features in the system grows, the maintenance becomes more complex. In such cases, to adopt a systematic approach, such as Software Product Line Engineering, is necessary. Existing systems are generally migrated to a product line, allowing systematic reuse of artefacts and easing maintenance. To this end, some approaches have been proposed in the literature in the last years. A mapping of works on this subject and the identification of some research gaps can lead to an improvement of such approaches. This paper describes the main outcomes of a systematic mapping study on the evolution and migration of systems to SPL. The main works found are presented and classified according to adopted strategy, artefacts used, and evaluation conducted. Analysis of the evolution along the past years are also presented. At the end, we summarize some trends and open issues to serve as reference to new researches.

Generating Integration Test Orders for Aspect Oriented Software with Multi-objective Algorithms

The problem known as CAITO refers to the determination of an order to integrate and test classes and aspects that minimizes stubbing costs. Such problem is NP-hard and to solve it efficiently, search based algorithms have been used, mainly evolutionary ones. However, the problem is very complex since it involves different factors that may influence the stubbing process, such as complexity measures, contractual issues and so on. These factors are usually in conflict and different possible solutions for the problem exist. To deal properly with this problem, this work explores the use of multi-objective optimization algorithms. The paper presents results from the application of two evolutionary algorithms - NSGA-II and SPEA2 - to the CAITO problem in four real systems, implemented in AspectJ. Both multi-objective algorithms are evaluated and compared with the traditional Tarjans algorithm and with a mono-objective genetic algorithm. Moreover, it is shown how the tester can use the found solutions, according to the test goals.

Reengineering legacy applications into software product lines: a systematic mapping

Software Product Lines (SPLs) are families of systems that share common assets allowing a disciplined reuse. Rarely SPLs start from scratch, instead they usually start from a set of existing systems that undergo a reengineering process. Many approaches to conduct the reengineering process have been proposed and documented in research literature. This scenario is a clear testament to the interest in this research area. We conducted a systematic mapping study to provide an overview of the current research on reengineering of existing systems to SPLs, identify the community activity in regarding of venues and frequency of publications in this field, and point out trends and open issues that could serve as references for future research. This study identified 119 relevant publications. These primary sources were classified in six different dimensions related to reengineering phases, strategies applied, types of systems used in the evaluation, input artefacts, output artefacts, and tool support. The analysis of the results points out the existence of a consolidate community on this topic and a wide range of strategies to deal with different phases and tasks of the reengineering process, besides the availability of some tools. We identify some open issues and areas for future research such as the implementation of automation and tool support, the use of different sources of information, need for improvements in the feature management, the definition of ways to combine different strategies and methods, lack of sophisticated refactoring, need for new metrics and measures and more robust empirical evaluation. Reengineering of existing systems into SPLs is an active research topic with real benefits in practice. This mapping study motivates new research in this field as well as the adoption of systematic reuse in software companies.

Extracting Variability-Safe Feature Models from Source Code Dependencies in System Variants

To effectively cope with increasing customization demands, companies that have developed variants of software systems are faced with the challenge of consolidating all the variants into a Software Product Line, a proven development paradigm capable of handling such demands. A crucial step in this challenge is to reverse engineer feature models that capture all the required feature combinations of each system variant. Current research has explored this task using propositional logic, natural language, and search-based techniques. However, using knowledge from the implementation artifacts for the reverse engineering task has not been studied. We propose a multi-objective approach that not only uses standard precision and recall metrics for the combinations of features but that also considers variability-safety, i.e. the property that, based on structural dependencies among elements of implementation artifacts, asserts whether all feature combinations of a feature model are in fact well-formed software systems. We evaluate our approach with five case studies and highlight its benefits for the software engineer.

Integration test of classes and aspects with a multi-evolutionary and coupling-based approach

The integration test of aspect-oriented systems involves the determination of an order to integrate and test classes and aspects, which should be associated to a minimal possible stubbing cost. To determine such order is not trivial because different factors influence on the stubbing process. Many times these factors are in conflict and diverse good solutions are possible. Due to this, promising results have been obtained with multi-objective and evolutionary algorithms that generally optimize two coupling measures: number of attributes and methods. However, the problem can be more effectively addressed considering as many as coupling measures could be associated to the stubbing process. Therefore, this paper introduces MECBA, a Multi-Evolutionary and Coupling-Based Approach to the test and integration order problem, which includes the definition of models to represent the dependency between modules and to quantify the stubbing costs. The approach is instantiated and evaluated considering four AspectJ programs and four coupling measures. The results represent a good trade-off between the objectives and an example of use of the obtained results shows how they can be used to reduce test effort and costs.

Empirical Studies on Application of Genetic Algorithms and Ant Colony Optimization for Data Clustering

Cluster analysis is used in several research areas to classify data sets in groups by their similar characteristics. Metaheuristic-based techniques, such as Genetic Algorithms (GAs) and Ant Colony Optimization (ACO), have been applied in order to increase the clustering algorithm performance. GA and ACO-based clustering algorithms are capable of efficiently and automatically forming natural groups from a pre-defined number of clusters. This paper presents a GA and an ACO algorithm to the clustering problem. Both algorithms were refined using local search in order to improve the clustering accuracy. The results are compared on numeric UCI databases.

Multi-objective reverse engineering of variability-safe feature models based on code dependencies of system variants

Maintenance of many variants of a software system, developed to supply a wide range of customer-specific demands, is a complex endeavour. The consolidation of such variants into a Software Product Line is a way to effectively cope with this problem. A crucial step for this consolidation is to reverse engineer feature models that represent the desired combinations of features of all the available variants. Many approaches have been proposed for this reverse engineering task but they present two shortcomings. First, they use a single-objective perspective that does not allow software engineers to consider design trade-offs. Second, they do not exploit knowledge from implementation artifacts. To address these limitations, our work takes a multi-objective perspective and uses knowledge from source code dependencies to obtain feature models that not only represent the desired feature combinations but that also check that those combinations are indeed well-formed, i.e. variability safe. We performed an evaluation of our approach with twelve case studies using NSGA-II and SPEA2, and a single-objective algorithm. Our results indicate that the performance of the multi-objective algorithms is similar in most cases and that both clearly outperform the single-objective algorithm. Our work also unveils several avenues for further research.