Very small entities (VSEs) of software development have had a significant demand and economic impact in recent years, because most of them are the software producers for medium and big companies in order to satisfy the growing demand of software. In this context, it is important to ensure that they produce quality software to successfully meet the market needs. This task relies on having the knowledge and the experience to implement proven practices, which are contained in quality models and standards, to be able to develop quality software, while increasing their productivity and keeping or reducing their costs. A description of the implementation of ISO/IEC 29110 in Mexico, specifically at Zacatecas State is presented. This implementation was done as a strategy to increase the competitiveness of them. The study includes both, the strategy established to deploy the knowledge and the method followed to implement the ISO/IEC 29110 in four VSEs that uses scrum methodology as agile approach. The results show that the implementation of ISO/IEC 29110 was easy and with a high acceptance due to the benefits detected in the reinforcement of the VSEs’ processes without changing the way they work and solving their problems.

The maintenance of software-intensive systems (SISs) must be undertaken to correct faults, improve the design, implement enhancements, adapt programmes such that different hardware, software, system features, and telecommunications facilities can be used, as well as to migrate legacy software. A lack of planning has been identified as one explanation for late and over budget software projects. An activity of planning is effort prediction. The goal of this study is to propose the application of a stochastic gradient boosting (SGB) model for predicting the SIS maintenance effort. We compare the SGB prediction accuracy with those obtained with statistical regression, neural network, support vector regression, decision trees, and association rules. We trained and tested the models with five SIS data sets selected from the International Software Benchmarking Standards Group Release 11. The SGB prediction accuracy was statistically better than the mentioned five models in the two larger data sets. We can conclude that a SGB can be applied to predict the maintenance effort of SISs coded in languages of the third generation and developed on either mainframes or multi-platform. The predicted effort corresponds to the aggregate of efforts obtained from the project team, project management, and project administration.

Gamification refers to the use of rules and game design techniques to involve and motivate people to achieve their goals. This work proposes an application development architecture that can generate game applications with gamification techniques. As proof of concept, the authors introduce Zeus, a platform for developing ruled-based serious game applications with gamification techniques. Zeus aims at generating gamified applications that can meet the learning goals set by users. These goals will be reflected through both learning and game attributes that users can personally select. To assess the functionality of the authors tool, they conduct a qualitative evaluation of four rule-based serious game applications developed with Zeus to help students learn about arithmetic. They make use of the Fun Toolkit to perform this evaluation in terms of the delivery of both fun and learning experiences. Their findings are encouraging in the context of learning basic arithmetic operations with Zeus game applications.

The variation points in software architecture arise as a result of the availability of large number of filters and component libraries. An integration of different architectural styles is crucial and necessary in the development of large-scale software application systems to handle the variation points. This article proposes a composite software architectural style for building application systems involving data streams, user interactivity, and dynamic mode. It uses a pattern within a pattern approach for combining the architectural styles. This approach provides flexibility to add or delete any filter or component at run time. In addition, the changes in the order of processing of the different filters or components can also be incorporated. The software architectural specification for any combination of input components and their order of processing is generated automatically. This specification acts as a baseline for the subsequent design and implementation phases of the application system. This model is generic and has been successfully validated for a prototype application system involving all the three modes of operation.

Every year software development industry requires a higher number of trained software engineers who are not only skilled programmers but also talented software projects managers. To deliver high-quality software projects, engineers require of the application of sound engineering competencies along with discipline. Obtaining those practices usually require years of experience. Companies are not prepared to invest this time on engineers resulting in a high percentage of deficient projects. Here, the authors present a bachelor-level competency-based approach that develops and evaluates such competencies during a challenge-based learning experience. In this way, the rate of successful projects where software engineers are involved will be higher, as they have obtained the appropriate competencies to deliver such projects.

Model-based testing (MBT) automates the design and generation of test cases from a model. This process includes model building, test selection criteria, test case generation, and test case execution stages. Current tools support this process at various levels of automation, most of them supporting three out of four stages. Among them is MBT4J, a platform that extends ModelJUnit with several techniques, offering a high level of automation for testing Java applications. In this study, the authors evaluate the efficacy of the MBT4J platform, in terms of the number of test cases generated, errors detected, and coverage metrics. A case study is conducted using two open-source Java systems from public repositories, and 15 different configurations. MBT4J was able to automatically generate five models from the source code. It was also able to generate up to 2025 unique test cases for one system and up to 1044 for the other, resulting in 167 and 349 failed tests, respectively. Transition and transition pair coverage reached 100% for all models. Code coverage ranged between 72 and 84% for the one system and between 59 and 76% for the other. The study found that Greedy and Random were the most effective testers for finding errors.

Workflow extraction a.k.a. Process mining (PM) is the connecting link between process modelling and data mining. Efforts led by multiple researchers and scientists to explore the opportunities of information extraction about self-loops and hidden transition of transactions or sub-processes using event log but unable to undertake any fruitful information. This study aims to display a computing approach that consolidates PM to the uncovered hidden transition of transactions from the event logs (from an extensive information system). The authors used an evolutionary algorithmic based computing approach to perform an extraction of the hidden transition of transactions from the extensive information system event logs. In this, author proposed and implement a customised version of the genetic algorithm uniquely tailored for the event logs, that used a Petri net, causal matrix, and workflow net for the intermediate processes. In this experimental study, author used different-different event logs collected from various information systems to validate the authors’ algorithm, i.e. running, repair, store, and hospital. In this experimental data-centric approach, a tailored evolutionary algorithm for the improvement of the software process as well as the software quality of legacy information systems is used and results are validated by different comparison matrices.

Nowadays, information technology services management (ITSM) has become strongly needed for every kind of organisations providing IT services for customers or for themselves. However, existing models (as CMMI-SVC, ITIL or ISO/IEC 20000) are strongly difficult to implement on very small organisations. The aim of this study is to validate an ITSM model specifically proposed for small organisations, the PCPS4SVC model. This model was developed considering the needs and constraints of small organisations and was verified against reference models using process mapping. In this study, the model was validated in three small enterprises, where it was tailored and implemented. As a result, the enterprises improved their IT services operation. Finally, it was determined than PCPS4SVC is a model easy-to-use and adequate for small enterprises.

Many of the software development projects are not successfully finished. In many cases, this is because the software requirements were not correctly identified. One of the factors that affect the elicitation of requirements is the proper selection of requirements elicitation techniques. Even when there is a lot of literature that proposes several requirements elicitation techniques, there is no reference process model that indicates the techniques that must be applied by a requirement's engineer to elicit the requirements of a mobile application for e-commerce. This gap covers both the elicitation of functional and non-functional requirements. Therefore, the authors propose a new model called reference process model for requirements elicitation for e-commerce mobile applications development (RPM-REFEMAD), which allows the elicitation of requirements based on the use of four elicitation techniques combined in ten steps. The proposed model was applied in the requirements elicitation of two e-commerce mobile applications in different scenarios. To visualise the results of the model, a survey was designed and applied to users/stakeholders who participated in the reference process model evaluation. The survey results indicated that RPM-REFEMAD model helps to elicit more quality and quantity functional and non-functional requirements, in comparison with the use of traditional requirements elicitation techniques.

The evolution of Smart Cities conveys continuous changes involving a great number of variables, which might hamper the development of evaluation tools and methodologies. Most of the metric models for Smart City are based on the selection of key performance indicators (KPI) according to the specific model objectives. As different organisations propose their own indicators generating different models, it is difficult to get a straightforward comparison among models. With the aim of dealing with this and other disadvantages, in this study, a framework based on the application of Data Science to the KPIs is proposed. This framework represents an infrastructure that goes through the treatment of Open Data, facilitating the evaluation of different models comparison intended for decision-making, and to the final stage of dispatching service reports. There are four components that integrate this framework (i) a tree structure to manage the KPIs; (ii) a designed JavaScript Object Notation document for service dispatch; (iii) Web applications for evaluations based on Smart People with four scenarios and; (iv) the infrastructure for reception and attention of reports.

Software product lines (SPL) emulate the industrial production lines that are capable of generating large volumes of products through reuse schemes and mass production. A multi product line (MPL) aims to reuse of several SPL. Feature models are often used to manage the existing resources of SPLs and define valid products through notations and relationships such as mandatory, optional, and alternative features. The main contribution of this study is a method to manage the variability of multiple SPL and generate a new portfolio of products for Internet of Things (IoT). For this, the problem of developing a universal feature model (FM) for an MPL from merging the FMs of the individual SPLs with a Search-Based Software Engineering (SBSE) technique is addressed. In addition, the authors propose a multi-objective optimisation model to maximise the reusability and compatibility between features and minimise the development cost. The model facilitates the design of an MPL-feature model. Authors’ empirical results show that the proposed model solved by genetic algorithms allows to configure a variety of software products and to determine the scope of the MPL.

In recent years, unmanned aerial vehicles (UAVs) have been used in different areas of applications such as rescue operations, surveillance, agriculture, aerial mapping, engineering applications and research, among others, in order to perform tasks with greater efficiency. This work focuses on the use of UAVs in the fishing sector in order to optimise the detection process of a shoal of fish. In this sense, the main idea is to perform images recognition using the images acquired through videos captured by UAV in the open sea; to achieve the objective the convolutional neural networks were used, a new dataset with different images captured through UAV videos in the open sea were taken into account, these classes correspond to dolphin, dolphin_pod, open_sea, and seabirds. The training tests were by transfer of learning using the following models: Inception V3, MobileNet V2, and NASNet-A (large) trained on TensorFlow platform. The experimental results show the detection performance with high-precision values in reasonable processing time. This study ends with a critical discussion of the experimental results.