Context:In software engineering (SE) experiments, the way in which a treatment is applied could affect results. Different interpretations of how to apply the treatment and decisions on treatment adherence could lead to different results when data are analysed.Objective:This paper aims to study whether treatment adherence has an impact on the results of an SE experiment.Method:The experiment used as test case for our research uses Test-Driven Development (TDD) and Incremental Test-Last Development, (ITLD) as treatments. We reported elsewhere the design and results of such an experiment where 24 participants were recruited from industry. Here, we compare experiment results depending on the use of data from adherent participants or data from all the participants irrespective of their adherence to treatments.Results:Only 40% of the participants adhere to both TDD protocol and to the ITLD protocol; 27% never followed TDD; 20% used TDD even in the control group; 13% are defiers (used TDD in ITLD session but not in TDD session). Considering that both TDD and ITLD are less complex than other SE methods, we can hypothesize that more complex SE techniques could get even lower adherence to the treatment.Conclusion:Both TDD and ITLD are applied differently across participants. Training participants could not be enough to ensure a medium to large adherence of experiment participants. Adherence to treatments impacts results and should not be taken for granted in SE experiments.

Abstract The way end-users interact with a system plays a crucial role in the high acceptance of software. Related to this, the concept of Intelligent User Interfaces has emerged as a solution to learn from user interactions with the system and adapt interfaces to the user’s characteristics and preferences. However, existing approaches to designing intelligent user interfaces are limited by their user models, which are not capable of representing each and every user characteristic valid for any context. This work aims to address this limitation by presenting a user model that can abstractly represent a wide set of user characteristics in any context of interaction. The model is based on a synthesis of previous works that have proposed specific user models. After the analysis of these works, a more sophisticated user model has been defined, including some required characteristics not existing in previous works. This model has been validated with 62 real end-users who have expressed the users’ characteristics that they consider as relevant to adapt the interaction. The results show that most of these characteristics can be represented by the proposed user model. This user model is the first step towards creating intelligent user interfaces that can adapt interactions to users with similar characteristics and preferences in similar contexts.

The usability requirements elicitation process is a difficult task that lacks methods to guide and help analysts, who are usually not experts at usability. This paper conducts an experiment with two replications to evaluate a method that elicits usability requirements based on structured interviews named UREM versus an unstructured method. The method consists of guided interviews by the analyst using decision trees. The tree is composed of questions and possible answers. Each question appears when there are different possible design alternatives, and each answer represents one of these alternatives. The tree also recommends the alternative that enhances the usability based on existing usability guidelines. We have conducted an experiment with two replications with 22 and 26 subjects playing two different roles in a within-subjects design. The analysts used a tree to guide the interview and elicit the requirements while the end users had to explain to the analyst the type of system to develop. During the interview, the analyst must design a paper prototype to be validated by the end user. For the analyst, the experiment measures the effectiveness of usability requirements elicitation, the effectiveness of the use of the usability guidelines, the efficiency of the elicitation process, and the satisfaction with the entire elicitation process. For the end user, the experiment measures the satisfaction with the designed prototype at the end of the interview. UREM yielded significantly better results for the effectiveness in the usability requirements elicitation process and for the effectiveness in the use of usability guidelines when compared to unstructured interviews. The use of UREM did not reduce the analysts’ efficiency and both analyst and end user remained the same satisfaction. Eliciting usability requirements is a difficult task if it is done with unstructured interviews and without usability recommendations.

Although the knowledge about human genomics is available to all scientists, information about this scientific breakthrough can often be difficult to fully comprehend and share. A Conceptual Schema of the Human Genome was previously developed to assist in describing human genome-related knowledge, by representing a holistic view of the relevant concepts regarding its biology and underlying mechanisms. This model should become helpful for any researcher who works with human genomics data. We, therefore, perform the process of ontological unpacking on a portion of the model, to facilitate domain understanding and data exchange among heterogeneous systems. The ontological unpacking is a transformation of an input conceptual model into an enriched model based on a foundational ontology. The preliminary analysis and enrichment process are supported by the ontological conceptual modeling language OntoUML, which has been applied previously to complex models to gain ontological clarity. The value of the used method is first assessed from a theoretical point of view: the transformation results in significant, diverse modeling implications regarding the characterization of biological entities, the representation of their changes over time, and, more specifically, the description of chemical compounds. Since the ontological unpacking process is costly, an empirical evaluation is conducted to study the practical implications of applying it in a real learning setting. A particularly complex domain such as metabolic pathways is either described by adopting a traditional conceptual model or explained through an ontologically unpacked model obtained from a traditional model. Our research is evidence that including a strong ontological foundation in traditional conceptual models is useful. It contributes to designing models that convey biological domains better than the original models.

Software-centric organisations design a loosely coupled organisation structure around strategic objectives, replicating this design to their business processes and information systems. Nowadays, dealing with business strategy in a model-driven development context is a challenge since key concepts such as the organisation's structure and strategic ends and means have been mostly addressed at the enterprise architecture level for the strategic alignment of the whole organisation, and have not been included into MDD methods as a requirements source. To overcome this issue, researchers have designed the LiteStrat, a business strategy modelling method compliant with MDD for developing information systems. This article presents an empirical comparison of LiteStrat and with i*, one of the most used models for strategic alignment in an MDD context. The article contributes with a literature review on the experimental comparison of modelling languages, the design of a study for measuring and comparing the semantic quality of modelling languages, and empirical evidence of the LiteStrat and i* differences. The evaluation consists of a 2 × 2 factorial experiment recruiting 28 undergraduate subjects. Significant differences favouring LiteStrat were found for models' accuracy and completeness, while no differences in modeller's efficiency and satisfaction were detected. These results yield evidence of the suitability of LiteStrat for business strategy modelling in a model-driven context.

Purpose: Mindfulness is a meditation technique whose main goal involves maintaining a calm mind and training attention by focusing only on a single thing (the support) at a time; this support is usually the practitioner's breathing. The practice of mindfulness aims to improve concentration and attention, which has proven useful in knowledge-intensive and stressful work environments like technological companies. This article aims to find empirical evidence on the positive effect of the practice of mindfulness on a sample of 56 helpdesk employees working for a consulting and information technology company (Accenture) with respect to: i) their attention awareness; ii) a set of key performance indicators (KPIs); and iii) the perceived benefits of mindfulness. Method: Of the 56 recruited employees, 29 worked as managers, and 27 worked as agents answering phone calls to solve software issues of the main information system of the Andalusian Health Service, a public organization with more than 115,000 employees. Mindfulness (the treatment) was applied to 26 subjects, while the other 30 subjects were the control group. For all subjects, their attention awareness was measured using the MAAS scale. Results: Both helpdesk managers and agents significantly improved their attention awareness with respect to the control group. Regarding organizational KPIs, in general, no evidence of significant differences between groups was detected, apart from the fact that the number of phone calls answered was significantly lower in the mindfulness group, probably due to a longer call duration caused by a deliberate better attention to the customer, but without degrading any other KPI. With respect to the perceived benefits of the treatment, the questionnaires show relevant improvements perceived by most employees after practicing mindfulness.Conclusions: We confirm that mindfulness improves attention awareness and benefits the working and personal life of helpdesk employees. However, further research is needed to identify a clear impact on productivity.

Context.In software engineering there is a widespread assumption that experience improves requirements analyst effectiveness, although empirical studies demonstrate the opposite.Aim.Determine whether experience (interviews, eliciting, development, professional) influences requirements elicitation using interviews.Method.We ran 12 quasi-experiments recruiting 124 subjects in which we measured analyst effectiveness as the number of items (i.e., concepts, rules, processes) correctly elicited. The experimental task was to elicit requirements using the open interview technique followed by the consolidation of the elicited information in domains with which the analysts were and were not familiar.Results.In unfamiliar domains, interview experience, requirements experience, development experience, and professional experience does not have any relationship with analyst effectiveness. In familiar domains, effectiveness varies depending on the type of experience. Interview experience has a positive effect, whereas professional experience has a moderate negative effect. Requirements experience appears to have a moderately positive effect; however, the statistical power of the analysis is insufficient to be able to confirm this point. Development experience has no effect.Conclusion.Experience impacts analyst effectiveness differently depending on the problem domain type (familiar, unfamiliar). Generally, experience does not account for all the observed variability in effectiveness, so there are other influential factors.

Context: There is a lack of empirical evidence on the differences between model-driven development (MDD), where code is automatically derived from conceptual models, and traditional software development method, where code is manually written. In our previous work, we compared both methods in a baseline experiment concluding that quality of the software developed following MDD was significantly better only for more complex problems (with more function points). Quality was measured through test cases run on a functional system. Objective: This paper reports six replications of the baseline to study the impact of problem complexity on software quality in the context of MDD. Method: We conducted replications of two types: strict replications and object replications. Strict replications were similar to the baseline, whereas we used more complex experimental objects (problems) in the object replications. Results: MDD yields better quality independently of problem complexity with a moderate effect size. This effect is bigger for problems that are more complex. Conclusions: Thanks to the bigger size of the sample after aggregating replications, we discovered an effect that the baseline had not revealed due to the small sample size. The baseline results hold, which suggests that MDD yields better quality for more complex problems.

Welcome to EICS, the Engineering Interactive Computing Systems community, PACMHCI/EICS journal, and annual conference! In this short article, we introduce newcomers to the field and to our community with an overview of what EICS is and how it positions with respect to other venues in Human-Computer Interaction, such as CHI, UIST, and IUI, highlighting its legacy and paying homage to past scientific events from which EICS emerged. We also take this opportunity to enumerate and exemplify scientific contributions to the field of Engineering Interactive Computing Systems, which we hope to guide researchers and practitioners towards making their future PACMHCI/EICS submissions successful and impactful in the EICS community.

Business Process Model and Notation (BPMN) provides organizations with a standard that facilitates further compression of the business process. BPMN focuses on the functional processes, leaving the development of interfaces to one side. Thereby, interface design usually depends on the subjective experience of the analyst. This article aims to propose a new method to generate user interfaces from BPMN models and Class Diagrams. The proposed method is based on the identification of different rules and makes use of stereotypes to extend BPMN notation. The rules have been extracted from seven existing projects on the Bizagi repository. Specifically, the proposal is based on the extraction of rules for generating user interfaces based on three widely used patterns: sequence pattern, exclusive decision pattern and synchronization pattern. As a result of our proposal, eleven new stereotypes have been added to BPMN notation. These stereotypes allow generating interfaces based on business process models. For a better understanding, the proposed stereotypes have been applied to an illustrative example. The results show that this work is a “step forward to” the automatic code generation from models.

Textual description of requirements is a specification technique that is widely used in industry, where time is key for success. How requirements are specified textually greatly depends on human factors. In order to study how requirements processing is affected by the level of detail in textual descriptions, this paper compares enriched textual requirements specifications with non-enriched ones. To do this, we have conducted an experiment in industry with 19 engineers of CAF (Construcciones y Auxiliares de Ferrocarril), which is a supplier of railway solutions. The experiment is a crossover design that analyzes efficiency, effectiveness, and perceived difficulty starting from a written specification of requirements that subjects must process in order to build software models. The results show that effectiveness and efficiency for enriched requirements are better, while non-enriched requirements are slightly more difficult to deal with. Therefore, even though enriched requirements require more time to be specified, the results are more successfully when using them.

gestUI is a model-driven method with tool support to define custom gestures and to include gesture-based interaction in existing user software system interfaces. So far, gestUI had been limited to the definition of the same gesture catalogue for all users of the software system. In this paper, we extend gestUI to permit individual users to define their own custom gesture catalogue and redefine some custom gestures in case of difficulty in using or remembering them. After extending gestUI, we applied technical action research from the FP7 CaaS project's Capability Design Tool with the aim of assessing its acceptance in an industrial setting. We also analysed its perceived ease-of-use and usefulness and gestUI's desirability level and user experience. The study shows that the tool can help improve the definition of custom gestures and the inclusion of gesture-based interaction in user interfaces of software systems.

[Background] Bug-fixing could be complex in industrial practice since thousands of products share features in their configuration. Despite the importance and complexity of bug-fixing, there is still a lack of empirical data about the difficulties found in industrial Software Product Lines (SPLs). [Aims] This paper aims to evaluate engineers' performance fixing errors and propagating the fixes to other configured products in the context of an industrial SPL. [Method] We designed and conducted an empirical study to collect data with regard to bug-fixing tasks within the context of a Induction Hob SPL in the BSH group, the largest manufacturer of home appliances in Europe. [Results] We found that effectiveness, efficiency and satisfaction got reached good values. Through interviews we also found difficulties related to unused features, cloning features unintentionally, detecting modified features, and propagating the fix when the source of the bug is the interaction between features. [Conclusions] The identified difficulties are relevant to know how to better apply SPLs in industry in the future.

Advanced interaction techniques are necessary to explore the potential of large data-volume systems. In this context, rich internet application patterns were defined, but usually reduced to the development of social web applications. However, other types of applications, such as data-analysis applications, require also advanced interaction solutions to assist users in making decisions and data-analysis. This paper identifies a set of problems emerged in the interaction between humans and data-analysis applications. We propose a set of guidelines for rich applications as a solution for these problems. As illustrative example of a real data-analysis environment, the paper focuses on a case study in the cultural heritage domain, highlighting the existing interaction problems and how they can be solved through the design guidelines proposed. The set of design guidelines allows to specify interfaces abstractly, creating a repository to solve interaction problems. These guidelines aim to serve as a basis for a future identification of new rich applications design patterns.

Usability is a quality attribute related to effectiveness, efficiency and satisfaction of the end-users when they interact with a system. There are sound usability design guidelines that aim to optimize the system usability. However, it is difficult to work with them if there is not a previous background in the Human-Computer Interaction area. Moreover, in the Software Engineering community, there are not specific methods to capture usability requirements. Usually, usability requirements are gathered with other non-functional requirements such as security or reliability, even though the goals of these non-functional requirements are not the same as the goals of usability. This problem arises when we aim to include the usability requirements capture in a Model Driven Development (MDD) process, where an effective capture technique with an unambiguous notation is needed to represent these requirements. In this paper, we propose a new method to capture usability requirements at the early stages of the software development process in such a way that non-experts in usability can use it. In a first step, experts in usability organize several interface design guidelines and usability guidelines in a tree structure. Next, these guidelines are shown to the analyst (non-expert in usability) through questions that she/he must ask the end-users. Answers to these questions mark the path through the tree structure. At the end of the process, if we gather all the end-user's answers, we have the usability requirements. These usability requirements could be transformed into a conceptual model of any existing MDD method by means of model to model transformations.

Web Engineering methods, specifically Model-driven Engineering (MDE) ones, are not yet ready to cope with user involvement features that Web 2.0 is demanding. In this work, Web 2.0 patterns are introduced using conceptual models that represent both interaction and functionality, since in Web 2.0 applications both features are intertwined to support the user involvement concern. This work also drafts how Web 2.0 patterns can be integrated into models of a model-driven Web Engineering method.

Usability is currently a key feature for developing quality systems. A system that satisfies all the functional requirements can be strongly rejected by end-users if it presents usability problems. End-users demand intuitive interfaces and an easy interaction in order to simplify their work. The first step in developing usable systems is to determine whether a system is or is not usable. To do this, there are several proposals for measuring the system usability. Most of these proposals are focused on the final system and require a large amount of resources to perform the evaluation (end-users, video cameras, questionnaires, etc.). Usability problems that are detected once the system has been developed involve a lot of reworking by the analyst since these changes can affect the analysis, design, and implementation phases. This paper proposes a method to minimize the resources needed for the evaluation and reworking of usability problems. We propose an early usability evaluation that is based on conceptual models. The analyst can measure the usability of attributes that depend on conceptual primitives. This evaluation can be automated taking as input the conceptual models that represent the system abstractly.

Traditionally, usability has been evaluated taking into account the user's satisfaction when interacting with the software system. However, in a Model-Driven Development (MDD) process, where conceptual models are the main resource for software system generation, the usability can potentially be evaluated at earlier stages. This work goes one step further proposing that certain usability attributes, specifically internal understandability attributes, can be measured from Conceptual Models. This work presents an empirical study carried out to evaluate the proposal. The goal of this study is to evaluate whether the value measured using our proposal is related to the understandability value perceived by the end user. From the analysis of the empirical results obtained, several weaknesses of the proposal are stated.