Thelma Elita Colanzi

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.

Applying search based optimization to software product line architectures: lessons learned

The Product-Line Architecture (PLA) is a fundamental SPL artifact. However, PLA design is a people-intensive and non-trivial task, and to find the best architecture can be formulated as an optimization problem with many objectives. We found several approaches that address search-based design of software architectures by using multi-objective evolutionary algorithms. However, such approaches have not been applied to PLAs. Considering such fact, in this work, we explore the use of these approaches to optimize PLAs. An extension of existing approaches is investigated, which uses specific metrics to evaluate the PLA characteristics. Then, we performed a case study involving one SPL. From the experience acquired during this study, we can relate some lessons learned, which are discussed in this work. Furthermore, the results point out that, in the case of PLAs, it is necessary to use SPL specific measures and evolutionary operators more sensitive to the SPL context.

A search-based approach for software product line design

The Product Line Architecture (PLA) can be improved by taking into account key factors such as feature modularization, and by continuously evaluating its design according to metrics. Search-Based Software Engineering (SBSE) principles can be used to support an informed-design of PLAs. However, existing search-based design works address only traditional software design not considering intrinsic Software Product Line aspects. This paper presents MOA4PLA, a search-based approach to support the PLA design. It gives a multi-objective treatment to the design problem based on specific PLA metrics. A metamodel to represent the PLA and a novel search operator to improve feature modularization are proposed. Results point out that the application of MOA4PLA leads to PLA designs with well modularized features, contributing to improve features reusability and extensibility. It raises a set of solutions with different design trade-offs that can be used to improve the PLA design.

Representation of software product line architectures for search-based design

The Product-Line Architecture (PLA) is the main artifact of a Software Product Line (SPL). Search-based approaches can provide automated discovery of near-optimal PLAs and make its design less dependent on human architects. To do this, it is necessary to adopt a suitable PLA representation to apply the search operators. In this sense, we review existing architecture representations proposed by related work, but all of them need to be extended to encompass specific characteristics of SPL. Then, the use of such representations for PLA is discussed and, based on the performed analysis, we introduce a novel direct PLA representation for search-based optimization. Some implementation aspects are discussed involving implementation details about the proposed PLA representation, constraints and impact on specific search operators. Ongoing work addresses the application of specific search operators for the proposed representation and the definition of a fitness function to be applied in a multi-objective search-based approach for the PLA design.

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.

Search based design of software product lines architectures

The Product-Line Architecture (PLA) is the main artifact of a Software Product Line (SPL). However, obtaining a modular, extensible and reusable PLA is a people-intensive and non-trivial task, related to different and possible conflicting factors. Hence, the PLA design is a hard problem and to find the best architecture can be formulated as an optimization problem with many factors. Similar Software Engineering problems have been efficiently solved by search-based algorithms in the field known as Search-based Software Engineering. The existing approaches used to optimize software architecture are not suitable since they do not encompass specific characteristics of SPL. To easy the SPL development and to automate the PLA design this work introduces a multi-objective optimization approach to the PLA design. The approach is now being implemented by using evolutionary algorithms. Empirical studies will be performed to validate the neighborhood operators, SPL measures and search algorithms chosen. Finally, we intend to compare the results of the proposed approach with PLAs designed by human architects.

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.

A Pattern-Driven Mutation Operator for Search-Based Product Line Architecture Design

The application of design patterns through mutation operators in search-based design may improve the quality of the architectures produced in the evolution process. However, we did not find, in the literature, works applying such patterns in the optimization of Product Line Architecture (PLA). Existing works offer manual approaches, which are not search-based, and only apply specific patterns in particular domains. Considering this fact, this paper introduces a meta-model and a mutation operator to allow the design patterns application in the search-based PLA design. The model represents suitable scopes, that is, set of architectural elements that are suitable to receive a pattern. The mutation operator is used with a multi-objective and evolutionary approach to obtain PLA alternatives. Quantitative and qualitative analysis of empirical results show an improvement in the quality of the obtained solutions.