Model merging in model-based engineering imposes high cognitive demands, as developers must identify, reconcile, and integrate changes across parallel artifacts. To explore the neural dynamics of this process, we conducted a controlled experiment with 35 developers performing merge tasks in two representational formats—sentential (Java code) and diagrammatic (UML class diagrams)—while EEG was continuously recorded. We clustered EEG signals into the four canonical microstate classes (A–D), which are well-established in cognitive neuroscience and represent recurring large-scale brain activity patterns. In alternating cycles of eyes-closed rest and task execution, we analyzed microstate dynamics and found diametrically opposite patterns depending on representation: in the sentential format, microstates A, C, and D increased significantly in duration, occurrence, and coverage, whereas in the diagrammatic format, A and C decreased, B declined moderately, and D showed reductions in occurrence and coverage. These findings reinforce the feasibility of microstate metrics as fine-grained biomarkers of cognitive overload in model-integration operations, paving the way for cognitively aware tools and adaptive environments capable of automatically adjusting abstraction levels or issuing high-load alerts.

The use of biometric data (BD) records promises to advance the software engineering field. The rapid adoption of wearable computing technology has widely increased the amount of BD records available. Several aspects about the use of BD records in software engineering field are unknown, such as body measurements used to support daily tasks, and empirical methods that are used to evaluate their benefits. Consequently, a thorough understanding of state-of-the-art techniques still remains limited. This article, therefore, aims at providing a classification and a thematic analysis of studies on the use of BD records in the context of software development. Moreover, it seeks to introduce a classification taxonomy, and pinpoints research gaps, challenges and trends. A systematic mapping of the literature was designed and performed based on well-established practical guidelines. In total, 40 primary studies were analysed and categorised, which were selected by applying a careful filtering process from a sample of 3930 studies to answer seven research questions. Over 77% of articles use more than one biometric aspect to analyse tasks performed by developers; over 47% of articles used eye-track sensor to analyse biometric factors, followed by brain-wearable sensors with 40%, skin sensor with 22%, cardiac sensor with 20%, and others fewer representatives; most studies analysed had their quality assessed as high; most studies were published in journal. This study provides a systematic map of studies that use BD records in software engineering, thereby serving as a basis for future research.

The field of software‐development effort estimation explores ways of defining effort through prediction approaches. Even though this field has a crucial impact on budgeting and project planning in industry, the number of works classifying and examining currently available approaches is still small. This article, therefore, presents a comprehensive overview of these approaches, and pinpoints research gaps, challenges and trends. A systematic mapping of the literature was designed and performed based on well‐established practical guidelines. In total, 120 primary studies were selected, analysed and categorised, after applying a careful filtering process from a sample of 3746 candidate studies to answer six research questions. Over 70% of the selected studies adopted multiple effort estimation approaches; over 45% adopted evaluation research as a research method; over 90% of the participants were students, rather than professionals; most studies had their quality assessed as high, and were most commonly published in journals. Our study benefits practitioners and researchers by providing a body of knowledge about the current literature, serving as a starting point for upcoming studies. This article reports challenges worth investigating, regarding the use of cognitive load and team interaction.

This study proposes SW‐Context, a model focused on defining context information for software, and storing this information in contextual history. The proposed model provides qualitative information concerning source code and design issues to support the daily activities of software developers, thereby improving their situation awareness. SW‐Context was evaluated through a case study in industry, in which 12 developers used it to support maintenance activities throughout one month. After this period, all developers filled out a qualitative questionnaire, which was formulated based on the well‐established technology acceptance model. The obtained results suggest that 67% of developers completely agree with the usefulness and ease of use of the SW‐Context. These results are encouraging and show the potential for implementing SW‐Context in broader software maintenance scenarios.

With the growing availability of devices capable of capturing information about their surroundings and the expansion of mobile connectivity, Internet of Things (IoT) solutions are increasingly been integrated into society. For IoT solutions to emerge successfully on the market, they will employ more than traditional mobile computing, but they will also require the use of everyday objects in an interconnected way. This interconnected world will support the intelligence in environments. In order to provide this infrastructure for environments, it will be necessary to propose platforms for software development based on context awareness and context processing. In this sense, this article proposes CMFrame, a framework for managing contextual information captured from physical environments using hierarchical and dynamic entities. CMFrame allows that entities to modify their hierarchical organization to manage environments and their related contexts. Contexts linked to each entity are also dynamic and can store different amounts of values at any time. The article presents the proposed framework and its evaluation through two applications focused on intelligent environments. The first is dedicated to monitor the movements of entities in an environment, and the second addresses energy monitoring. The scientific contribution of CMFrame is the proposal to abstract the management of dynamic and hierarchical context histories through a framework.

Context: In recent years, several studies explored different facets of the developers' cognitive load while executing tasks related to software engineering. Researchers have proposed and assessed different ways to measure developers' cognitive load at work and some studies have evaluated the interplay between developers' cognitive load and other attributes such as productivity and software quality. Problem: However, the body of knowledge about developers' cognitive load measurement is still dispersed. That hinders the effective use of developers' cognitive load measurements by industry practitioners and makes it difficult for researchers to build new scientific knowledge upon existing results. Objective: This work aims to pinpoint gaps providing a classification and a thematic analysis of studies on the measurement of cognitive load in the context of software engineering. Method: We carried out a Systematic Mapping Study (SMS) based on well-established guidelines to investigate nine research questions. In total, 33 articles (out of 2,612) were selected from 11 search engines after a careful filtering process. Results: The main findings are that (1) 55% of the studies adopted electroencephalogram (EEG) technology for monitoring the cognitive load; (2) 51% of the studies applied machine-learning classification algorithms for predicting cognitive load; and (3) 48% of the studies measured cognitive load in the context of programming tasks. Moreover, a taxonomy was derived from the answers of research questions. Conclusion: This SMS highlighted that the precision of machine learning techniques is low for realistic scenarios, despite the combination of a set of features related to developers' cognitive load used on these techniques. Thus, this gap makes the effective integration of the measure of developers' cognitive load in industry still a relevant challenge.

Model merging plays a chief role in many software engineering activities, e.g. evolving Unified Modelling Language (UML) models for adding new features. Software developers may evolve UML models using merge relationships. However, given the growing heterogeneity of merge strategies and the UML limitations for expressing merge relationship, it is particularly challenging for them to specify merge relationships. Consequently, developers, end up expressing improperly merge relationships. Today, the UML can neither specify the semantics nor the order in which merge relationships must be performed. Developers are unable to specify the semantics and order in which merge relationships must be performed. This study, therefore, proposes UML2Merge, which is a UML extension for expressing merge relationship. The UML2Merge was evaluated through an empirical study with 10 participants for investigating its effects on the merge effort, the correctness of merge relationships, and the participant's acceptance. The collected data suggest that the UML2Merge is proper to express merge relationships by requiring a low merge effort, producing elevated correctness of merge relationships, and having a high acceptance of the participants. The results are encouraging and show the potential for using UML2Merge to express the evolution of UML models through merge relationships.

The integration of feature models plays a key role in many tasks in software development, such as evolving Software Product Lines (SPL) to add new features.However, based on our experience in previous empirical studies, one of the main shortcomings to the widespread adoption of integration techniques is the lack of empirical knowledge about its effects on the effort of analysts and developers.This problem applies to integration techniques involving a set of operations (union and intersection) as well as the relationships between features and their elements.This article, therefore, reports on a controlled experiment that investigated the effort of (1) applying the integration techniques of feature models by professionals and students, and (2) detecting and resolving inconsistencies in the output-integrated models.The integration effort was evaluated through 10 evolution scenarios.The main results suggest that there is no significant difference regarding (1) the integration effort invested by professionals and students to produce a desired integrated model, and (2) the correctness rate of the integrations performed by professionals and students.

Context: The Unified Modeling Language (UML) has become the standard for modeling software.Several surveys on the UML usage have been proposed in recent years.However, none of them explores the UML use in specific regional scope, and thus little is known about the practices and perceptions of UML use from the perspective of practitioners in the Brazilian industry.Objective: This paper reports on a survey focused on identifying the state-of-the-practice of the Brazilian industry for what concerns the UML usage in real-world settings.Method: In total, 222 practitioners from 140 different Information Technology companies have answered an on-line (or printed) questionnaire concerning their UML use experiences, the difficulty in adopting UML and what should be done to increase the UML adoption in practice.Result: The results show that: (1) 60 participants (28.2%) have used UML in their daily work, while 73.2% have not; (2) 55.41% of the surveyed participants did not disagree with the statement that UML is the "lingua franca" in software modeling; (3) 61.26% reported to find that the automatic creation of UML diagrams to represent a big picture of the system under development would be useful to boost UML use.Conclusion: The UML is not often used in the work life of participants.In addition, no relationship was identified between the use of UML and the participant company being a software factory.

Even though existing heuristics and specification-based techniques support composing design models, it is still considered a time-consuming and highly intensive task.In addition, there is a lack of studies exploring the effects of composition techniques on software developers' affective state and development effort.This study reports a pilot study to investigate these effects while developers apply composition techniques to detect and resolve inconsistencies in output-composed models.In this sense, a widely known wearable EEG headset, namely Emotiv EPOC, with 14 channels was used, while developers made use of heuristic-based and specification-based composition techniques to evolve design models.Our results suggest that using heuristic-based techniques produced a higher effect on the developers' affectivity, compared to specification-based techniques.Moreover, the higher the effects on the developers' affectivity, the higher the odds to invest less effort and produce correctly composed design models.

Academia and industry are increasingly concerned with producing general-purpose model composition techniques to support many software engineering activities, e.g., evolving UML design models or reconciling conflicting models.However, the current techniques fail to provide flexible and reusable architectures, a comprehensive understanding of the critical composition activities, and guidelines about how developers can use and extend them.These limitations are one of the main reasons why state-of-the-art techniques are often unable to aid the development of new composition tools.To overcome these shortcomings, this paper, therefore, proposes a flexible, component-based architecture for aiding the development of composition techniques.Moreover, an intelligible composition workflow is proposed to help developers to improve the understanding of crucial composition activities and their relationships.Our preliminary evaluation indicated that the proposed architecture could support composition tools for UML class, sequence, and component diagrams.

Context: Model comparison plays a central role in many software engineering activities.However, a comprehensive understanding about the state-of-art is still required.Goal: This paper, therefore, aims at classifying, identifying publication fora, and performing thematic analysis of the current literature in model comparison for creating an extensive and detailed understanding about this area, thereby determining gaps by graphing and pinpointing in which research areas and for which study types a shortage of publications still exits.Method: We have conducted a systematic mapping study to scrutinize those contributions produced over time, which research topics have most investigated, and which research methods that have been applied.For this, we have followed well-established empirical guidelines to define and apply a systematic mapping study.Results: The results are: (1) majority of studies (14 out of 40) provide generic model comparison techniques, rather than comparison techniques for UML diagrams; (2) a categorization and quantification of the current studies in a variety of dimensions; and (3) an overview of current research topics and trends.

Context: Model comparison plays a central role in many software engineering activities. However, a comprehensive understanding about the state-of-the-art is still required. Goal: This paper aims at classifying and performing a thematic analysis of the current literature. Method: For this, we have followed well-established empirical guidelines to define and perform a systematic mapping study. Results: Some studies (14 out of 40) provide generic model comparison techniques, rather than specific ones for UML diagrams. Conclusion: Fine-grained techniques are still required to support ever-present and complex model comparison tasks during the evolution of design models.

Model composition plays a central role in many software engineering activities, e.g., evolving design models to add new features. To support these activities, developers usually rely on model composition heuristics. The problem is that the models to-be-composed usually conflict with each other in several ways and such composition heuristics might be unable to properly deal with all emerging conflicts. Hence, the composed model may bear some syntactic and semantic inconsistencies that should be resolved. As a result, the production of the intended model is an error-prone and effort-consuming task. It is often the case that developers end up examining all parts of the output composed model instead of prioritizing the most critical ones, i.e., those that are likely to be inconsistent with the intended model. Unfortunately, little is known about indicators that help developers (1) to identify which model is more likely to exhibit inconsistencies, and (2) to understand which composed models require more effort to be invested. It is often claimed that software systems remaining stable over time tends to have a lower number of defects and require less effort to be fixed than unstable systems. However, little is known about the effects of software stability in the context of model evolution when supported by composition heuristics. This paper, therefore, presents an exploratory study analyzing stability as an indicator of inconsistency rate and resolution effort on model composition activities. Our findings are derived from 180 compositions performed to evolve design models of three software product lines. Our initial results, supported by statistical tests, also indicate which types of changes led to lower inconsistency rate and lower resolution effort.

The importance of model composition in model-centric software development is recognized by researchers and practitioners. However, the lack of empirical evidence about the impact of model composition techniques on developers' effort is a key impairment for their adoption in real-world design settings. Software engineers are left without any guidance on how to properly use certain model techniques in a way that effectively reduces their development effort. This work aims to address this problem by: (1) providing empirical evidence on model composition effort through a family of experimental studies; (2) defining quantitative indicators to objectively assess key attributes of model composition effort; (3) deriving a method to support the systematic application of composition techniques; and (4) conceiving a new model composition technique to overcome the problems identified throughout the experimental evaluations.

In this paper we explore the use of formal ontology in model comparison. Most techniques used in the MDA community are essentially syntactic, and rely on typographical hints, such as attribute data types and labels to determine similarity. We have adapted and incorporated an ontology alignment technique, as the means to obtain more precise and reliable similarity measurements between model elements, a fundamental issue in model comparison and composition.

With the advent of the Model Driven Development (MDD), models are replacing code as the major artifact in software development. These models are typically specified using OMG's Unified Modeling Language (UML) . In MDD, model transformation and model composition are two essential model management tasks. Model transformation has been extensively researched while model composition still needs further investigation. The goal of this paper is to propose a guidance for model composition (specifically for class diagrams composition) based on match rules, model transformation rules and model composition strategy. Moreover, we present UML Profile for Model Composition and make an analysis about UML's composition mechanism.