Miguel Goulão

The i* community has published guidelines, including model layout guidelines, for the construction of models. Our goal is to evaluate the effect of the layout guidelines on the i* novice stakeholders’ ability to understand and review i* models. We conducted a quasi-experiment where participants were given two understanding and two reviewing tasks. Both tasks involved a model with a bad layout and another model following the i* layout guidelines. We evaluated the impact of layouts by combining the success level in those tasks and the required effort to accomplish them. Effort was assessed using time, perceived complexity (with NASA TLX), and eye-tracking data. Participants were more successful in understanding than in reviewing tasks. However, we found no statistically significant difference in the success, time taken, or perceived complexity, between tasks conducted with models with a bad layout and models with a good layout. Most participants had little to no prior knowledge in i*, making them more representative of stakeholders with no requirements engineering expertise. They were able to understand the models fairly well after a short tutorial, but struggled when reviewing models. Adherence to the existing i* layout guidelines did not significantly impact i* model understanding and reviewing performance.

A rapidly changing market leads to software systems with highly volatile requirements. In many cases, new demands in software can often be met by extending the functionality of systems already in operation. By modularizing volatile requirements that can be altered at the client’s initiative or according to market demands, we can build a stepping-stone for management of requirements change. The volatility must be managed in a way that reduces the time and costs associated with updating a system to meet the new requirements. In this chapter, we present an approach for handling volatile concerns during early life cycle software modeling. The key insight is that techniques for aspect-oriented software development can be applied to modularize volatility and to weave volatile concerns into the base software artifacts.

The usability of requirements engineering (RE) techniques has been recognised as a key factor for their successful adoption by industry. RE techniques must be accessible to stakeholders with different backgrounds, so they can be empowered to effectively and efficiently contribute to building successful systems. When selecting an appropriate requirements engineering technique for a given context, one should consider the usability supported by each of the candidate techniques. The first step towards achieving this goal is to gather the best evidence available on the usability of RE approaches by performing a systematic literature review, to answer one research question: How is the usability of requirements engineering techniques and tools addressed? We systematically review articles published in the Requirements Engineering Journal, one of the main sources for mature work in RE, to motivate a research roadmap to make RE approaches more accessible to stakeholders with different backgrounds.

Domain-Specific Languages (DSLs) are claimed to bring important productivity improvements to developers,
when compared to General-Purpose Languages (GPLs). The increased Usability is regarded as one of the key benefits of DSLs when compared to GPLs, and has an important impact on the achieved productivity of the DSL users. So, it is essential to build in good usability while developing the DSL. The purpose of this proposal is to contribute to the systematic activity of Software Language Engineering by focusing on the
issue of the Usability evaluation of DSLs. Usability evaluation is often skipped, relaxed, or at least omitted from papers reporting development of DSLs. We argue that a systematic approach based on User Interface experimental validation techniques should be used to assess the impact of new DSLs. For that purpose, we propose to merge common Usability evaluation processes with the DSL development process. In order to provide reliable metrics and tools we should reuse and identify good practices that exist in Human-Computer
Interaction community.

There are several tools currently available in the i * community. These tools have different features and purposes. Choosing the most adequate tool for a specific modelling situation can be a challenge. To overcome this diffculty, we present a systematic comparison of the i * tools listed in the i * wiki page, according to their features, syntax coverage and semantic analysis support. Our comparison highlights the
different strengths of those tools, to help identifying situations for which each tool might be particularly useful. We contribute with an aggregated vision of current i * tool support to the body of knowledge of the i * community. In addition, this comparison also helps identifying opportunities for further evolution of the surveyed tools.

Software reuse is a broadly accepted practice to improve software development quality and productivity. Although an object of study in software engineering since the late sixties, achieving effective reuse remains challenging for many software development organizations. This paper reports a systematic mapping study on how reusability relates to other non-functional requirements and how different contextual factors influence the success of a reuse initiative. The conclusion is that the relationships are discussed rather informally, and that human, organizational, and technological domain factors are extremely relevant to a particular reuse context. This mapping study highlights the need for further research to better understand how exactly the different non-functional requirements and context factors affect reusability.

Scenario modeling can be realized through different perspectives. In UML, scenarios are often modeled with activity models, in an early stage of development. Later, sequence diagrams are used to detail object interactions. The migration from activity diagrams to sequence diagrams is a repetitive and error-prone task. Model-Driven Development (MDD) can help streamlining this process, through transformation rules. Since the information in the activity model is insufficient to generate the corresponding complete sequence model, manual refinements are required. Our goal is to compare the relative effort of building the sequence diagrams manually with that of building them semi-automatically. Our results show a decrease in the number of operations required to build and refine the sequence model of approximately 64% when using MDD, when compared to the manual approach.

Prediction models of software change requests are useful for supporting rational and timely resource
allocation to the evolution process. In this paper we use a time series forecasting model to predict software
maintenance and evolution requests in an open source software project (Eclipse), as an example of projects with seasonal release cycles. We build an ARIMA model based on data collected from Eclipse’s change request tracking system since the project’s start. A change request may refer to defects found in the software, but also to suggested improvements in the system under scrutiny. Our model includes the identification of seasonal patterns and tendencies, and is validated through the forecast of the change requests evolution for the next 12 months. The usage of seasonal information significantly improves the estimation ability of this model, when compared to other ARIMA models found in the literature, and does so for a much longer estimation period. Being able to accurately forecast the change requests’ evolution over a fairly long time period is an important ability for enabling adequate process control in maintenance activities, and facilitates
effort estimation and timely resources allocation. The approach presented in this paper is suitable for projects with a relatively long history, as the model building process relies on historic data.

Domain Specic Languages (DSLs) are claimed to contribute
to increment productivity, while reducing the required maintenance and
programming expertise. In this context, the usability of these languages
becomes a major issue: if the language is not easy to learn and use, it is
unlikely to be successfully adopted.

It is important to foster high quality DSLs during its engineering process.
We argue that a systematic approach based on User Interface Experi-
mental validation techniques should be used to assess the impact of the
introduction of DSLs in the productivity of DSL user. Productivity can
be fostered by assessing important usability attributes early in the lan-
guage construction . This work's contribution, besides highlighting the
problem of the absence of systematic approaches for experimental vali-
dation of DSLs in general, is to identify existing evaluation approaches
that can be adapted from the eld of User Interfaces.

Domain Specific Languages (DSLs) are claimed to increment productivity, while reducing the required maintenance and programming expertise. In this context, DSLs usability is a key factor for its successful adoption.

In this paper, we propose a systematic approach based on User Interfaces Experimental validation techniques to assess the impact of the introduction of DSLs on the productivity of domain experts. To illustrate this evaluation approach we present a case study of a DSL for High Energy Physics (HEP).

The DSL on this case study, called Pheasant (PHysicist’s EAsy Analysis Tool), is assessed in contrast with a pre-existing baseline, using General Purpose Languages (GPLs) such as C++. The comparison combines quantitative and qualitative data, collected with users from a real-world setting. Our assessment includes Physicists with programming experience with two profiles; ones with no experience with the previous framework used in the project and other experienced.

This work’s contribution highlights the problem of the absence of systematic approaches for experimental validation of DSLs. It also illustrates how an experimental approach can be used in the context of a DSL evaluation during the Software Languages Engineering activity, with respect to its impact on effectiveness and efficiency.

Model-driven engineering (MDE) is believed to have a significant impact in software quality. However, researchers and practitioners may have a hard time locating consolidated evidence on this impact, as the available information is scattered in several different publications. Our goal is to aggregate consolidated findings on quality in MDE, facilitating the work of researchers and practitioners in learning about the coverage and main findings of existing work as well as identifying relatively unexplored niches of research that need further attention. We performed a tertiary study on quality in MDE, in order to gain a better understanding of its most prominent findings and existing challenges, as reported in the literature. We identified 22 systematic literature reviews and mapping studies and the most relevant quality attributes addressed by each of those studies, in the context of MDE. Maintainability is clearly the most often studied and reported quality attribute impacted by MDE. Eighty out of 83 research questions in the selected secondary studies have a structure that is more often associated with mapping existing research than with answering more concrete research questions (e.g., comparing two alternative MDE approaches with respect to their impact on a specific quality attribute). We briefly outline the main contributions of each of the selected literature reviews. In the collected studies, we observed a broad coverage of software product quality, although frequently accompanied by notes on how much more empirical research is needed to further validate existing claims. Relatively, little attention seems to be devoted to the impact of MDE on the quality in use of products developed using MDE.

For years the development of software artifacts was the sole domain of developers and project
managers. However, experience has taught us that the Users play a very important role in
software development and construction. On Domain Specific Languages the inclusion of the
domain experts directly in the development cycle is a very important characteristic, as they have
often an important role in making and constraining the domain of the language.
DSLs are credited with increased productivity and ease of use, but this fact is hardly ever proven.
Moreover, usability tests are frequently only performed at the final stages of the project when
changes have a significant impact on the budget. To help prevent this, in this paper we present a
pattern language for evaluating the usability of DSLs. Our patterns can help show how to use an
iterative usability validation development strategy to produce DSLs that can achieve a high
degree of usability.

Objective: To present an overview on the current state of the art concerning metrics-based
quality evaluation of software components and component assemblies.
Method: Comparison of several approaches available in the literature, using a framework
comprising several aspects, such as scope, intent, definition technique, and maturity.
Results: The identification of common shortcomings of current approaches, such as
ambiguity in definition, lack of adequacy of the specifying formalisms and insufficient
validation of current quality models and metrics for software components.
Conclusions: Quality evaluation of components and component-based infrastructures
presents new challenges to the Experimental Software Engineering community.

Reviews on software engineering literature have shown an insufficient experimental validation of claims, when compared to the standard practice in other well-established sciences. Poor validation of software engineering claims increases the risks of introducing changes in the software process of an organization, as the potential benefits assessment is based on hype, rather than on facts. The community lacks highly disseminated experimental best practices. We contribute with a model of the experimental software engineering process that is aligned with recent proposals for best practices in experimental data dissemination. The model can be used in the definition of software engineering experiments and in comparisons among experimental results.

Goal-Oriented Requirements Engineering (GORE) has received increasing attention over the past few years.
There are several goal-oriented approaches, each one using different kinds of models. We argue that it would be useful to relate them or even perform transformations among them automatically, in order to understand their similarities and differences, their advantages and disadvantages, allowing a possible migration or comparison between approaches. This is something that has not received enough attention. In this paper
we propose the definition and implementation of goal model transformations between i* and KAOS. As an immediate contribution, the approach can be used to migrate from one goal model to another through automatic model transformations. This approach also contributes to relate the concepts of i* and KAOS models and will help, for example, a development team in making the decision on which approach to follow, according to the nature of the project and the expressiveness of an approach to represent certain concepts
(e.g., obstacles are represented explicitly in KAOS, but not in i*). Another contribution is to facilitate communication among members of the same team, if they are specialized in different approaches.

Goal-oriented Requirements Engineering approaches have become popular in the Requirements Engineering community as they provide expressive modelling languages for requirements elicitation and analysis. However, as a common challenge, such approaches are still struggling when it comes to managing the accidental complexity of their models. Furthermore, those models might be incomplete, resulting in insufficient information for proper understanding and implementation. In this paper, we provide a set of metrics, which are formally specified and have tool support, to measure and analyse complexity and completeness of goal models, in particular social goal models (e.g. i⁎). Concerning complexity, the aim is to identify refactoring opportunities to improve the modularity of those models, and consequently reduce their accidental complexity. With respect to completeness, the goal is to automatically detect model incompleteness. We evaluate these metrics by applying them to a set of well-known system models from industry and academia. Our results suggest refactoring opportunities in the evaluated models, and provide a timely feedback mechanism for requirements engineers on how close they are to completing their models.