Test Case Prioritization (TCP) techniques are a key factor in reducing the regression testing costs even more when Continuous Integration (CI) practices are adopted. TCP approaches based on failure history have been adopted in this context because they are more suitable for CI environment constraints: test budget and test case volatility, that is, test cases may be added or removed over the CI cycles. Promising approaches are based on Reinforcement Learning (RL), which learns with past prioritization, guided by a reward function. In this work, we introduce a TCP approach for CI environments based on the sliding window method, which can be instantiated with different Machine Learning (ML) algorithms. Unlike other ML approaches, it does not require retraining the model to perform the prioritization and any code analysis. As an alternative for the RL approaches, we apply the Random Forest (RF) algorithm and a Long Short Term Memory (LSTM) deep learning network in our evaluation. We use three time budgets and eleven systems. The results show the applicability of the approach considering the prioritization time and the time between the CI cycles. Both algorithms take just a few seconds to execute. The RF algorithm obtained the best performance for more restrictive budgets compared to the RL approaches described in the literature. Considering all systems and budgets, RF reaches Normalized Average Percentage of Faults Detected (NAPFD) values that are the best or statistically equivalent to the best ones in around 72% of the cases, and the LSTM network in 55% of them. Moreover, we discuss some implications of our results for the usage of the algorithms evaluated.

Summary The use of mutation testing for mobile applications (apps for short) is still a challenge. Mobile apps are usually event‐driven and encompass graphical user interfaces (GUIs) and a complex execution environment. Then, they require mutant operators to describe specific apps faults, and the automation of the mutation process phases like execution and analysis of the mutants is not an easy task. To encourage research addressing such challenges, this paper presents results from a mapping study on mutation testing for mobile apps. Following a systematic plan, we found 16 primary studies that were analysed according to three aspects: (i) trends and statistics about the field; (ii) study characteristics such as focus, proposed operators and automated support for the mutation testing phases; and (iii) evaluation aspects. The great majority of studies (98%) have been published in the last 3 years. The most addressed language is Java, and Android is the only operating system considered. Mutant operators of GUI and configuration types are prevalent in a total of 138 operators found. Most studies implement a supporting tool, but few tools support mutant execution and analysis. The evaluation conducted by the studies includes apps mainly from the finance and utility domain. Nevertheless, there is a lack of benchmarks and more rigorous experiments. Future research should address other specific types of faults, languages, and operating systems. They should offer support for mutant execution and analysis, as well as to reduce the mutation testing cost and limitations in the mobile context.

Mutation testing is an effective approach to evaluate and strengthen software test suites, but its adoption is currently limited by the mutants’ execution computational cost. Several strategies have been proposed to reduce this cost (a.k.a. mutation cost reduction strategies), however none of them has proven to be effective for all scenarios since they often need an ad-hoc manual selection and configuration depending on the software under test (SUT). In this paper, we propose a novel multi-objective evolutionary hyper-heuristic approach, dubbed Sentinel, to automate the generation of optimal cost reduction strategies for every new SUT. We evaluate Sentinel by carrying out a thorough empirical study involving 40 releases of 10 open-source real-world software systems and both baseline and state-of-the-art strategies as a benchmark. We execute a total of 4,800 experiments, and evaluate their results with both quality indicators and statistical significance tests, following the most recent best practice in the literature. The results show that strategies generated by Sentinel outperform the baseline strategies in 95 percent of the cases always with large effect sizes. They also obtain statistically significantly better results than state-of-the-art strategies in 88 percent of the cases, with large effect sizes for 95 percent of them. Also, our study reveals that the mutation strategies generated by Sentinel for a given software version can be used without any loss in quality for subsequently developed versions in 95 percent of the cases. These results show that Sentinel is able to automatically generate mutation strategies that reduce mutation testing cost without affecting its testing effectiveness (i.e., mutation score), thus taking off from the tester’s shoulders the burden of manually selecting and configuring strategies for each SUT.

Continuous Integration (CI) environments have been increasingly adopted in the industry to allow frequent integration of software changes, making software evolution faster and cost-effective. In such environments, Test Case Prioritization (TCP) techniques play an important role to reduce regression testing costs, establishing a test case execution order that usually maximizes early fault detection. Existing works on TCP in CI environments (TCPCI) present some limitations. Few pieces of work consider CI particularities, such as the test case volatility, that is, they do not consider the dynamic environment of the software life-cycle in which new test cases can be added or removed (discontinued), characteristic related to the Exploration versus Exploitation (EvE) dilemma. To solve such a dilemma an approach needs to balance: i) the diversity of test suite; and ii) the quantity of new test cases and test cases that are error-prone or that comprise high fault-detection capabilities. To deal with this, most approaches use, besides the failure-history, other measures that rely on code instrumentation or require additional information, such as testing coverage. However, to maintain the information updated can be difficult and time-consuming, not scalable due to the test budget of CI environments. In this context, and to properly deal with the TCPCI problem, this work presents an approach based on Multi-Armed Bandit (MAB) calledCOLEMAN(Combinatorial VOlatiLEMulti-Armed BANdit). The TCPCI problem falls into the category of volatile and combinatorial MAB, because multiple arms (test cases) need to be selected, and they are added or removed over the cycles. We conducted an evaluation considering three time budgets and eleven systems. The results show the applicability of our approach and thatCOLEMANoutperforms the most similar approach from literature in terms of early fault detection and performance.

Software refactoring changes the structure of a program without modifying its external behavior, generally intending to improve software quality attributes. However, refactoring is a complex activity and, many times, a composition of refactorings is necessary. Besides, some code elements are refactored similarly, considering the kind and frequency of refactorings applied. Works in the refactoring literature usually investigate the impact and understanding of an individual refactoring, neglecting that developers have to apply more than one refactoring operation to reach their goals. There is a lack of studies to identify and characterize refactoring patterns. To fulfill this gap, this work explores the use of unsupervised learning, particularly cluster analysis, to group elements (Java classes) that are refactored similarly in software repositories. We used a total of 1435 projects and applied the K-Means algorithm to group classes that received the same refactoring with the same frequency. We obtained a set of seven clusters. Then, the main refactoring compositions associated with each cluster are analyzed to identify the corresponding pattern. Each pattern is described and also characterized using a set of metrics. The great majority of refactoring compositions include only one kind of refactoring, applied with low frequency. If we consider compositions including more than one type of refactorings, combinations of Extract Superclass and Pull Up Method are the most frequent.

Software systems have been continuously evolved and delivered with high quality due to the widespread adoption of automated tests. A recurring issue hurting this scenario is the presence of flaky tests, a test case that may pass or fail non-deterministically. A promising, but yet lacking more empirical evidence, approach is to collect static data of automated tests and use them to predict their flakiness. In this paper, we conducted an empirical study to assess the use of code identifiers to predict test flakiness. To do so, we first replicate most parts of the previous study of Pinto et al. (MSR 2020). This replication was extended by using a different ML Python platform (Scikit-learn) and adding different learning algorithms in the analyses. Then, we validated the performance of trained models using datasets with other flaky tests and from different projects. We successfully replicated the results of Pinto et al. (2020), with minor differences using Scikit-learn; different algorithms had performance similar to the ones used previously. Concerning the validation, we noticed that the recall of the trained models was smaller, and classifiers presented a varying range of decreases. This was observed in both intra-project and inter-projects test flakiness prediction.

Companies have adopted modern code review as a key technique for continuously monitoring and improving the quality of software changes. One of the main motivations for this is the early detection of design impactful changes, to prevent that design-degrading ones prevail after each code review. Even though design degradation symptoms often lead to changes' rejections, practices of modern code review alone are actually not sufficient to avoid or mitigate design decay. Software design degrades whenever one or more symptoms of poor structural decisions, usually represented by smells, end up being introduced by a change. Design degradation may be related to both technical and social aspects in collaborative code reviews. Unfortunately, there is no study that investigates if code review stakeholders, e.g, reviewers, could benefit from approaches to distinguish and predict design impactful changes with technical and/or social aspects. By analyzing 57,498 reviewed code changes from seven open-source systems, we report an investigation on prediction of design impactful changes in modern code review. We evaluated the use of six ML algorithms to predict design impactful changes. We also extracted and assessed 41 different features based on both social and technical aspects. Our results show that Random Forest and Gradient Boosting are the best algorithms. We also observed that the use of technical features results in more precise predictions. However, the use of social features alone, which are available even before the code review starts (e.g., for team managers or change assigners), also leads to highly-accurate prediction. Therefore social and/or technical prediction models can be used to support further design inspection of suspicious changes early in a code review process. Finally, we provide an enriched dataset that allows researchers to investigate the context behind design impactful changes during the code review process.

Microservices are small and independently deployable services. They can be developed on different platforms and communicate via lightweight protocols, what makes them highly interoperable. The interoperability between microservices, as well as their reuse and customization needs make this kind of systems adequate to constitute a Software Product Line. However, there is no automatic approach to support the designing of Microservices-Based Product Lines (MBPLs). To move towards the development of MBPLs, this work presents an approach, named MOEA4MBPL, to extract Feature Models (FMs) from a set of microservices-based systems. These FMs intent to leverage interoperability, enabling the practitioners to reason about reuse and/or customization of functionalities. The proposed approach is based on multi-objective evolutionary algorithms, optimizing three objectives, namely precision and recall of products denoted by an FM, and conformance with existing dependencies between microservices. MOEA4MBPL was evaluated with six microservices-based systems, using the algorithms NSGA-II and SPEA2. Our approach was capable of finding FMs with good trade-off values of precision and recall, satisfying all dependencies among the microservices. SPEA2 found better fronts of solutions than NSGA-II, but the latter always executed faster and could find single solutions closer to an ideal solution than the former.

To perform the variability testing of Software Product Lines (SPLs) a set of products, represented in the Feature Model (FM), should be selected. Such selection is impacted by conflicting factors and has been efficiently solved by Evolutionary Multi-objective Algorithms in combination with hyper-heuristics. However, many times there is a cost budget or coverage level to be satisfied during the test, which are difficult to be incorporated as objective functions. Due to this, the choice of the best solution to be used in practice is not always easy. To deal with this situation, this paper introduces a preference-based hyper-heuristic approach to solve this problem. The approach implements the preference-based algorithm r-NSGA-II working with the random and FRRMAB selection methods. This last one uses a reward function based on r-dominance concept that takes into consideration a Reference Point provided by the tester. Our approach outperforms existing approaches, as well as the traditional algorithm r-NSGA-II, generating a reduced number of non-interesting solutions from the tester's point of view, that is, considering the provided Region of Interest (ROI).

The Feature Model (FM) is a fundamental artifact of the Software Product Line (SPL) engineering, used to represent commonalities and variabilities, and also to derive products for testing. However, the test of all features combinations (products) is not always possible in practice. Due to the growing complexity of the applications, only a subset of products is usually selected. The selection is generally based on combinatorial testing, to test features interactions. This kind of selection does not consider different classes of faults that can be present in the FM. The application of a fault-based approach, such as mutation-based testing, can increase the probability of finding faults and the confidence that the SPL products match the requirements. Considering that, this paper introduces a mutation approach to select products for the feature testing of SPLs. The approach can be used similarly to a test criterion in the generation and assessment of test cases. It includes (i) a set of mutation operators, introduced to describe typical faults associated to the feature management and to the FM; and (ii) a testing process to apply the operators. Experimental results show the applicability of the approach. The selected test case sets are capable to reveal other kind of faults, not revealed in the pairwise testing.

The selection of products for testing Software Product Lines (SPLs) is an optimization problem. The goal is to select a possible minimum set of products that satisfies testing criteria, such as, pairwise and mutation testing. Multi-objective Evolutionary Algorithms (MOEAs) have been successfully used to solve this problem and other ones related to software development. However, the use of MOEAs demands setting a number of control parameters and selection of genetic operators, to which the algorithm performance is often very sensitive. Adaptive Operator Selection (AOS) methods, such as Upper Confidence Bound (UCB) based ones can help in this task. UCB methods used with Multi-objective Evolutionary Algorithm Based on Decomposition with Dynamical Resource Allocation (MOEA/D-DRA) have presented promising results, but they are underexplored in the Search Based Software Engineering (SBSE) field. To contribute to this research area and to solve efficiently the product selection problem, this paper investigates the use of different AOS UCB-based methods with MOEA/D-DRA. The idea is to reduce effort spent by the tester. Some parameters and evolutionary operators can be automatically set. The approach is empirical evaluated using four instances and three UCB methods. The UCB methods present similar results and outperform the canonical version of MOEA/D-DRA.

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.

The adoption of the layered architectural style can improve the Product Line Architecture (PLA) design by providing a better organization of the elements, flexibility and maintainability. Search based optimization approaches can also benefit the PLA design, by generating PLA alternatives associated with the best trade-offs between different measures (fitness) related to cohesion, coupling and feature modularization. However, the usage of existing search operators changes the PLA organization, and consequently may violate layered rules, impacting negatively in the architecture understanding. In order to solve this problem, this work introduces search operators that consider the layered architectural style rules. Results show that the proposed operators contribute to obtain better solutions, maintaining the adopted style, with better or equivalent fitness values.

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.

Multi-objective evolutionary algorithms (MOEAs) have been efficiently applied to Search-Based Software Engineering (SBSE) problems. However, skilled software engineers waste significant effort designing such algorithms for a particular problem, adapting them, selecting operators and configuring parameters. Hyper-heuristics can help in these tasks by dynamically selecting or creating heuristics. Despite of such advantages, we observe a lack of works regarding this subject in the SBSE field. Considering this fact, this work introduces HITO, a Hyper-heuristic for the Integration and Test Order Problem. It includes a set of well-defined steps and is based on two selection functions (Choice Function and Multi-armed Bandit) to select the best low-level heuristic (combination of mutation and crossover operators) in each mating. To perform the selection, a quality measure is proposed to assess the performance of low-level heuristics throughout the evolutionary process. HITO was implemented using NSGA-II and evaluated to solve the integration and test order problem in seven systems. The introduced hyper-heuristic obtained the best results for all systems, when compared to a traditional algorithm.

The Product Line Architecture (PLA) design is a complex task, influenced by many factors such as feature modularization and PLA extensibility, which are usually evaluated according to different metrics. Hence, the PLA design is an optimization problem and problems like that have been successfully solved in the Search-Based Software Engineering (SBSE) area, by using metaheuristics such as Genetic Algorithm. Considering this fact, this paper introduces a tool named OPLA-Tool, conceived to provide computer support to a search-based approach for PLA design. OPLA-Tool implements all the steps necessary to use multi-objective optimization algorithms, including PLA transformations and visualization through a graphical interface. OPLA-Tool receives as input a PLA at the class diagram level, and produces a set of good alternative diagrams in terms of cohesion, feature modularization and reduction of crosscutting concerns.

The Product Line Architecture (PLA) design is a multi-objective optimization problem that can be properly solved in the Search Based Software Engineering (SBSE) field. However, the PLA design has specific characteristics. For example, the PLA is designed in terms of features and a highly modular PLA is necessary to enable the growth of a software product line. However, existing search based design approaches do not consider such needs. To overcome this limitation, this paper introduces a feature-driven crossover operator that aims at improving feature modularization. The proposed operator was applied in an empirical study using the multi-objective evolutionary algorithm named NSGAII. In comparison with another version of NSGAII that uses only mutation operators, the feature-driven crossover version found a greater diversity of solutions (potential PLA designs), with higher feature-based cohesion, and less feature scattering and tangling.

The Product Line Architecture (PLA) design is a multi-objective optimization problem that can be properly solved with search-based algorithms. However, search-based PLA design is an incipient research field. Due to this, works in this field have addressed main points to solve the problem: adequate representation, specific search operators and suitable evaluation fitness functions. Similarly what happens in the search-based design of traditional software, existing works on search-based PLA design use NSGA-II, without evaluating the characteristics of this algorithm, such as the use of crossover operator. Considering this fact, this paper reports results from a comparative analysis of two algorithms, NSGA-II and PAES, to the PLA design problem. PAES was chosen because it implements a different evolution strategy that does not employ crossover. An experimental study was carried out with nine PLAs and results of the conducted study attest that NSGA-II performs better than PAES in the PLA design context.

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.

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.

Evolutionary testing has successfully applied search based optimization algorithms to the test data generation problem. The existing works use different techniques and fitness functions. However, the used functions consider only one objective, which is, in general, related to the coverage of a testing criterion. But, in practice, there are many factors that can influence the generation of test data, such as memory consumption, execution time, revealed faults, and etc. Considering this fact, this work explores a multiobjective optimization approach for test data generation. A framework that implements a multi-objective genetic algorithm is described. Two different representations for the population are used, which allows the test of procedural and object-oriented code. Combinations of three objectives are experimentally evaluated: coverage of structural test criteria, ability to reveal faults, and execution time.

Genetic programming (GP) models adapt better to the reliability curve when compared with other traditional, and non-parametric models. In a previous work, we conducted experiments with models based on time, and on coverage. We introduced an approach, named genetic programming and Boosting (GPB), that uses boosting techniques to improve the performance of GP. This approach presented better results than classical GP, but required ten times the number of executions. Therefore, we introduce in this paper a new GP based approach, named (¿ + ¿) GP. To evaluate this new approach, we repeated the same experiments conducted before. The results obtained show that the (¿ + ¿) GP approach presents the same cost of classical GP, and that there is no significant difference in the performance when compared with the GPB approach. Hence, it is an excellent, less expensive technique to model software reliability.

Software testing is a fundamental software engineering activity for quality assurance that is also traditionally very expensive. To reduce efforts of testing strategies, some design metrics have been used to predict the fault-proneness of a software class or module. Recent works have explored the use of machine learning (ML) techniques for fault prediction. However most used ML techniques can not deal with unbalanced data and their results usually have a difficult interpretation. Because of this, this paper introduces a multi-objective particle swarm optimization (MOPSO) algorithm for fault prediction. It allows the creation of classifiers composed by rules with specific properties by exploring Pareto dominance concepts. These rules are more intuitive and easier to understand because they can be interpreted independently one of each other. Furthermore, an experiment using the approach is presented and the results are compared to the other techniques explored in the area.

Software reliability is one of the most important quality characteristics for almost all systems. The use of a software reliability model to estimate and predict the system reliability level is fundamental to ensure software quality. However, the selection of an appropriate model for a specific case can be very difficult for project managers. This is because, there are several models that can be used and none has proved to perform well considering different projects and databases. Each model is valid only if its assumptions are satisfied. To aim at the task of choosing the best software reliability model for a dataset, this paper presents a meta-learning approach and describes experimental results from the use of a neural network meta classifier for selection among different kind of reliability models. The obtained results validate the idea and are very promising.

Abstract Parallel programs present some features such as concurrency, communication and synchronization that make the test a challenging activity. Because of these characteristics, the direct application of traditional testing is not always possible and adequate testing criteria and tools are necessary. In this paper we investigate the challenges of validating message‐passing parallel programs and present a set of specific testing criteria. We introduce a family of structural testing criteria based on a test model. The model captures control and data flow of the message‐passing programs, by considering their sequential and parallel aspects. The criteria provide a coverage measure that can be used for evaluating the progress of the testing activity and also provide guidelines for the generation of test data. We also describe a tool, called ValiPar, which supports the application of the proposed testing criteria. Currently, ValiPar is configured for parallel virtual machine (PVM) and message‐passing interface (MPI). Results of the application of the proposed criteria to MPI programs are also presented and analyzed. Copyright © 2008 John Wiley & Sons, Ltd.

Software reliability models are used to estimate the probability that a software fails at a given time. They are fundamental to plan test activities, and to ensure the quality of the software being developed. Each project has a different reliability growth behavior, and although several different models have been proposed to estimate the reliability growth, none has proven to perform well considering different project characteristics. Because of this, some authors have introduced the use of Machine Learning techniques, such as neural networks, to obtain software reliability models. Neural network-based models, however, are not easily interpreted, and other techniques could be explored. In this paper, we explore an approach based on genetic programming, and also propose the use of boosting techniques to improve performance. We conduct experiments with reliability models based on time, and on test coverage. The obtained results show some advantages of the introduced approach. The models adapt better to the reliability curve, and can be used in projects with different characteristics.

Software effort and cost estimation is a very important activity that includes very uncertain elements. In the context of object oriented software, traditional methods and metrics were extended to help managers in this activity. The metric use case points (UCP) is an example of metric that can be used. UCP considers functional aspects of the use case (UC) model, widely used in most organizations in the early phases of the development. However, the metric UCP presents some limitations mainly related to the granularity of the UC. To overcome these limitations, this paper introduces two metrics, also based on UCs. The first one, named USP (use case size points), considers the internal structures of the UC and better captures its functionality. The second one, named FUSP (fuzzy use case size points), considers concepts of the fuzzy set theory to create gradual classifications that better deal with uncertainty. Results from an empirical evaluation show the applicability and some advantages of the proposed metrics

Software reliability models are used to estimate the probability of a software fails along the time. They are fundamental to plan test activities and to ensure the quality of the software being developed. Two kind of models are generally used: time or test coverage based models. In our previous work, we successfully explored Genetic Programming (GP) to derive reliability models. However, nowadays Boosting techniques (BT) have been successfully applied with other Machine Learning techniques, including GP. BT merge several hypotheses of the training set to get better results. With the goal of improving the GP software reliability models, this work explores the combination GP and BT. The results show advantages in the use of the proposed approach.

Web service is a modern technology commonly used to integrate software projects among different platforms, operating systems or even programming languages. This distributed and heterogeneous nature complicates the testing activity which is, in general, expensive and effort demanding. Adequate and cost effective testing methods are needed for Web services. An extended approach based on XML messages perturbation has been introduced to test pairs of Web services. Perturbation operators produce modified XML messages, which are used as test cases. This paper explores the use of such promising approach by introducing new perturbation operators for SOAP messages and describing a supporting tool, named SMAT-WS. An experimental study was accomplished with this tool. The obtained results allow an evaluation of the perturbation operators regarding cost and efficacy

Genetic Programming (GP) is a powerful software induction technique that can be applied to solve a wide variety of problems. However, most researchers develop tailor-made GP tools for solving specific problems. These tools generally require significant modifications in their kernel to be adapted to other domains. In this paper, we explore the Grammar-Guided Genetic Programming (GGGP) approach as an alternative to overcome such limitation. We describe a GGGP based framework, named Chameleon, that can be easily configured to solve different problems. We explore the use of Chameleon in two domains, not usually addressed by works in the literature: in the task of mining relational databases and in the software testing activity. The presented results point out that the use of the grammar-guided approach helps us to obtain more generic GP frameworks and that they can contribute in the explored domains.

XML is a language frequently used for data representation and interchange in Web-based applications. In most cases, the XML documents must conform to a schema that defines the type of data that is accepted by a Web application. In this sense, an error in the schema or in the XML document can lead to failures in the application; the use of testing approaches, criteria and specific tools to ensure the reliability of data in the XML format is fundamental. We present a testing approach that helps to reveal faults in XML schemas. The test process involves generating XML documents with some modifications with respect to the original XML document and using queries to these documents to validate the schema. The XML documents and queries are generated according to a set of fault classes defined for the XML schemas. A case study applying the proposed approach is described and the results are presented.

Reliability models are very useful to estimate the probability of the software fail along the time. Several different models have been proposed to estimate the reliability growth, however, none of them has proven to perform well considering different project characteristics. In this work, we explore genetic programming (GP) as an alternative approach to derive these models. GP is a powerful machine learning technique based on the idea of genetic algorithms and has been acknowledged as a very suitable technique for regression problems. The main motivation to choose GP for this task is its capability of learning from historical data, discovering an equation with different variables and operators. In this paper, experiments were conducted to confirm this hypotheses and the results were compared with traditional and neural network models.

Estimating software effort and costs is a very important activity that includes very uncertain elements. The concepts of the fuzzy set theory has been successfully used for extending metrics such as FP and reducing human influence in the estimation process. However, when we consider object-oriented technologies, other models, such as the use case model, are used to represent the specification in the early stages of development. New metrics based on this model were proposed and the application of the fuzzy set theory in this context is also very important. This work introduces the metric FUSP (fuzzy use case size points) that allows gradual classifications in the estimation by using fuzzy numbers. Results of a study case show some advantages and limitations of the proposed metric.