Documents written in natural language constitute a major part of the artefacts produced during the software engineering life cycle. Studies indicate that more than 80% of enterprise data is stored in some sort of unstructured form, mainly as text. Therefore, the growth of user-generated content, especially from social media, provides a huge amount of data which allows discovering the experiences, opinions, and feelings of users. Text mining refers to the set of tools, techniques, and algorithms adopted to extract useful information from unstructured data. Considering that Portuguese ranks among the ten most spoken languages, and it is the second most common in Twitter, this study aims to map current primary studies that relate to the application of text mining for Portuguese. A systematic mapping method was applied and 6075 primary studies were retrieved up to the year 2014. A total of 203 studies were included, from which more than 60% analyse texts written in Brazilian variant. The majority of studies focus on the text classification task. Support vector machine and Naïve Bayes appear as main the algorithms. Folha de São Paulo and Público newspapers appear as main corpora, followed by the Portuguese Attorney General's Office corpus and Twitter.

Service selection is a very challenging core task in service oriented architecture-based application (SOABA) development. Service selection is based on user's business need and budget available. A large amount of effort is invested in selecting the most preferred service from a pool of similar services available in the market by various service providers. To propose a direction for solving the problem of service selection effort (SSE) estimation in SOABA is the objective in this study. An algorithm for SSE estimation powered by information entropy weight (IEW) fuzzy comprehensive evaluation model is presented in this study wherein the synthesis performance of each candidate service is evaluated to select the most preferred service. Larger the synthesis performance of a candidate service, higher the chances of its selection and less will be the effort invested. An empirical study is presented that assess the 24 significant parameters that affect SSE estimation by using proposed algorithm. This approach is comprehensible and rational and well suited for SOABA. The results obtained via this proposed method suggest its applicability and usefulness for real-world applications. The proposed work also explains why IEW method is useful for SSE estimation along with the research gap in existing common evaluation methods.

Coping with ever increasing complexity is one of the main challenges of software engineering. Applying proven practices through established software processes is a viable way to introduce consistency and tame complexity. The SEMAT is a novel meta-process aiming to encompass commonalities across a disparate set of different software processes. The goal is to create a framework, giving proper jumpstart to any proprietary customisation. This is crucial, as various types of software demand diverse software processes (there is no one-size-fits-all method). On the other hand, all processes strive for a disciplined application of valid development and maintenance routines.

This paper analyses a set of methodologies for several software development lines to recognise common and specific development practices. A special emphasise is put on the test-driven development (TDD), showcasing its significant contribution in improving the total quality of the end product. The SEMAT Kernel is extended with additional set of alphas, which are elaborated in detail including containing states, transitions and dependencies. The TDD practice activities were analysed for each introduced alpha as state transition triggers. Consequently, the high-level development plan is expanded with newly defined alphas, providing a powerful tool for performing all sorts of system analysis.

The reserves or protected areas have a fundamental role in the biodiversity of the planet. The main objective of the reserves is to protect areas where a large number of animal and plant species coexist, considering also, a set of abiotic factors such as water, soil and sunlight. This research solves the budget-constrained maximal covering location (BCMCL) problem. The aim of BCMCL problem is to maximise the number of species to be protected by the constraints of a limited budget and the costs that have to protect each area. The BCMCL problem is an NP-hard optimisation problem with a binary domain. For the resolution of BCMCL problem, the authors propose a binary version of African buffalo optimisation (ABO). The tests performed to solve the BCMCL problem have used a set of 12 test instances that have been solved by the algorithm binary ABO. Moreover, eight transfer functions have been applied and experienced in the binary version of ABO. The algorithms migrating birds optimisation, random descent and steepest descent are used to compare the best results obtained by ABO. Finally, the results show that the binary version of ABO has competitive results compared with other algorithms.

In modern web-based applications, an increasing amount of source code is generated dynamically at runtime. Web applications commonly execute dynamically generated code (DGC) emitted by third-party, black-box generators, run at remote sites. Web developers often need to adapt DGC before it can be executed: embedded HTML can be vulnerable to cross-site scripting attacks; an API may be incompatible with some browsers; and the program's state created by DGC may not be persisting. Lacking any systematic approaches for adapting DGC, web developers resort to ad-hoc techniques that are unsafe and error-prone. This study presents an approach for adapting DGC systematically that follows the program-transformation-by-example paradigm. The proposed approach provides predefined, domain-specific before/after examples that capture the variability of commonly used adaptations. By approving or rejecting these examples, web developers determine the required adaptation transformations, which are encoded in an adaptation script operating on the generated code's abstract syntax tree. The proposed approach is a suite of practical JavaScript program adaptations and their corresponding before/after examples. The authors have successfully applied the approach to real web applications to adapt third-party generated JavaScript code for security, browser compatibility, and persistence.

Knowledge capitalisation directs the activities of use, exploitation and reuse of knowledge for performing new tasks, including technical and management aspects. Specifically, considering software development, knowledge capitalisation demands an analysis that considers four complementary and interdependent perspectives on knowledge: value, security, utility and status. These perspectives focus on different facets according to which knowledge can be analysed, in addition to provide a foundation for a better awareness on knowledge and the necessary treatment. This study presents an empirical study with the purpose of evaluating the relevance of these perspectives by using a survey questionnaire distributed to software development companies in the Brazilian market. Three hundred thirty nine individuals from different 339 companies were subjects of such a study. The results indicate that the perspectives are relevant for evaluating knowledge and reinforcing the relevance of exploring knowledge capitalisation in software development; therefore, there is a clear understanding of how the knowledge assets involved can be analysed or assessed.

The majority of flaws found in software originates in the system requirements specification stage. The use of domain-specific languages has shown to be a valuable resource in this part of the process, since they help to establish communication standards, enable automation and bring productivity and quality gains, in spite of their limited vocabulary. This study proposes the implementation of language of use case to automate models (LUCAM), a domain-specific language that allows specification of textual use cases and semi-automated generation of use case diagrams, class diagrams and sequence diagrams through LUCAMTool. To verify the feasibility of the proposed solution, tests were performed in both simulated and real environments so as to comprise a variety of scenarios observed in systems development. The approach assists in the requirement analysis and modelling, minimising existing problems in natural language specification, such as the dependence on the knowledge of specialists, uncertainty, ambiguity and complexity.

Publishing, analysing or properly accessing the abundant information resulting largely from experimental studies in the biomedical domain are current challenges for the research community. Problems with the extraction of relevant information, redundant data, and lack of associations or provenance are good examples of the main concerns. The innovative nanopublication publishing strategy tries to overcome these issues by representing the essential pieces of publishable information on the Semantic Web. However, existing methods to create these Resource Description Framework-based data snippets are based on complex scripting procedures, hindering their use by the community. Therefore, novel and automated strategies are needed to explore the evident value of nanopublications and to enable data attribution mechanisms, an important feature for data owners. To solve these challenges, the authors introduce the second generation of the COEUS open-source application framework (http://bioinformatics.ua.pt/coeus/), an automated platform to integrate heterogeneous scientific outcomes into nanopublications. This results in seamless integration, making data accessible and citable at the same time. No additional scripting methods are needed. A validation of a nanopublishing pipeline is described to demonstrate the system functionalities, integrating and publishing common biomedical achievements into the Semantic Web ecosystem.

The aim of this study is to detect transportation modes of people by using smartphone sensors. Therefore, a mobile application was developed for this purpose and global positioning system (GPS), accelerometer, and gyroscope sensor data were collected while the subjects were walking, running, biking, and travelling by bus or by car. The application was running for over 8 h. Sensor data were tagged with 12 s intervals and 2500 patterns were obtained. Eleven features were selected from the data set and machine learning methods were applied to detect transportation modes using different sensor combinations. Performances of the methods were discussed in terms of accuracy ratios. Best results were obtained from GPS, accelerometer, and gyroscope sensor combination data using logistic regression method with 99.6% accuracy rate.

This study presents a new method for handwritten keyword spotting. The innovation in this paper is to provide a model based on neural network architecture and an output based on the margin. At first, a neural network is designed such that its output determines whether a test word as an input is spotted or rejected. The intended neural network has one input layer, two middle layers, and one output layer. Another innovation in this study is optimising neural network weights based on swarm optimisation method. This optimisation model is used to train the neural network, so that the output has adequate margin for classification. The new components of the proposed classifier include new particle coding and new fitness function. Two layers are considered in coding particle, one for activating and deactivating neural network nodes and the other layer for acquiring proper values for weights. Different experiments with variety of parameters were designed for the multi-layer perceptron neural network. The experiments on three datasets: AMA Arabic dataset, IAM English dataset, and IFN/Farsi dataset yielded 83, 77, and 69% values, respectively, in the best condition. The results demonstrate that the proposed method has been better than the previous ones.