Miguel Goulão

Complex software systems inherently require a variety of models used in all of the development stages. A general concern is to guarantee consistency and traceability among these models. Model-driven development (MDD) can help tackle this concern. Although MDD has been mainly used in later development stages, it is relatively unexplored in requirements engineering. In this article, the authors discuss how to
leverage MDD to support consistency and traceability in requirements modeling. To illustrate this, they apply MDD to goaloriented requirements engineering (GORE) by making bidirectional mappings between two well-known GORE approaches (i* and KAOS). The result is an interoperable framework that can be used to migrate from one goal model to another through automatic model transformations, keeping consistency and traceability, so requirements engineers can make the best use of each approach.

Feature Models are commonly used to specify commonalities and variabilities in Software Product Lines (SPL). Our goal is to enhance feature modeling with traceability and improved support for crosscutting concerns. While traceability will show the features’ requirement-origins, providing means to reason about
their existence, crosscutting concerns will be handled through advanced modularity mechanisms (e.g. aspects), making the impact of changes to SPL models less difficult to understand and analyze. The result is Theme/SPL, a novel SPL requirements technique based on a concern-driven approach (Theme/Doc). Theme/SPL includes the proposal of a domain-specific language for specifying Theme/Doc models and uses model-driven development to generate automatically feature models from them. We show the applicability of the technique through a case study using a within-group design to evaluate the final results and tools developed.

The development of web applications that manipulate geo-referenced information is often supported by Application Programming Interfaces (APIs), allowing a fast development cycle for high quality applications. APIs can be used by programmers with different expertise levels and choosing an adequate API may
have a dramatic impact on the productivity achieved by those programmers. Our goal is to compare maps APIs with respect to their usability. We compare three different APIs: the Google Maps JavaScript API, the ArcGIS API for JavaScript, and the OpenLayers JavaScript Mapping Library. Our comparison is supported by
a set of software metrics and is performed in two orthogonal ways: the comparison of three implementations of the same system prototype, each using one of the APIs under scrutiny; the comparison of the APIs specifications. The main results of the study are related to the size of the APIs, with the Google API being significantly smaller than the others.

Background: Often, claims in Component-Based Development (CBD) are only sup-
ported by qualitative expert opinion, rather than by quantitative data. This contrasts
with the normal practice in other sciences, where a sound experimental validation of
claims is standard practice. Experimental Software Engineering (ESE) aims to bridge
this gap. Unfortunately, it is common to find experimental validation efforts that are
hard to replicate and compare, to build up the body of knowledge in CBD.

Objectives: In this dissertation our goals are (i) to contribute to evolution of ESE, in
what concerns the replicability and comparability of experimental work, and (ii) to ap-
ply our proposals to CBD, thus contributing to its deeper and sounder understanding.

Techniques: We propose a process model for ESE, aligned with current experimen-
tal best practices, and combine this model with a measurement technique called
Ontology-Driven Measurement (ODM). ODM is aimed at improving the state of prac-
tice in metrics definition and collection, by making metrics definitions formal and ex-
ecutable, without sacrificing their usability. ODM uses standard technologies that can
be well adapted to current integrated development environments.

Results: Our contributions include the definition and preliminary validation of a pro-
cess model for ESE and the proposal of ODM for supporting metrics definition and
collection in the context of CBD. We use both the process model and ODM to perform
a series experimental works in CBD, including the cross-validation of a component
metrics set for JavaBeans, a case study on the influence of practitioners expertise in
a sub-process of component development (component code inspections), and an ob-
servational study on reusability patterns of pluggable components (Eclipse plug-ins).
These experimental works implied proposing, adapting, or selecting adequate ontolo-
gies, as well as the formal definition of metrics upon each of those ontologies.

Limitations: Although our experimental work covers a variety of component models
and, orthogonally, both process and product, the plethora of opportunities for using
our quantitative approach to CBD is far from exhausted.

Conclusions: The main contribution of this dissertation is the illustration, through
practical examples, of how we can combine our experimental process model with
ODM to support the experimental validation of claims in the context of CBD, in a re-
peatable and comparableway. In addition, the techniques proposed in this dissertation
are generic and can be applied to other software development paradigms.

Domain Specic Languages (DSLs) can contribute to increment productivity, while reducing the required maintenance and programming expertise. We hypothesize that Software Languages Engineering (SLE) developers consistently skip, or relax, Language Evaluation. Based on the experience of engineering other types of software products, we assume that this may potentially lead to the deployment of inadequate languages. The fact that the languages already deal with concepts from the problem domain, and not the solution domain, is not enough to validate several issues at stake, such as its expressiveness, usability,
effectiveness, maintainability, or even the domain expert's productivity while using them. We present a systematic review on articles published in top ranked venues, from 2001 to 2008, which report DSLs' construction, to characterize the common practice. This work conrms our initial hypothesis and lays the ground for the discussion on how to include a systematic approach to DSL evaluation in the SLE process.

We can regard Domain-Specific Languages (DSLs) as User Interfaces (UIs) because they bridge the gap
between the domain experts and the computation platforms. Usability of DSLs by domain experts is a key
factor for their successful adoption. The few reports supporting improvement claims are persuasive, but
mostly anecdotal. Systematic literature reviews show that evidences on the effects of the introduction of
DSLs are actually very scarce. In particular, the evaluation of usability is often skipped, relaxed, or at
least omitted from papers reporting the development of DSLs. The few exceptions mostly take place at
the end of the development process, when fixing problems is already too expensive. A systematic
approach, based on techniques for the experimental evaluation of UIs, should be used to assess suitability
of new DSLs. This chapter presents a general experimental evaluation model, tailored for DSLs’
experimental evaluation, and instantiates it in several DSL’s evaluation examples.

Background: Aspect-oriented programming (AOP) is an
emerging programming paradigm whose focus is about improving
modularity, with an emphasis on the modularization of
crosscutting concerns.

Objective: The goal of this paper is to assess the extent to which
an AOP language – ObjectTeams/Java (OT/J) – improves the
modularity of a software system. This improvement has been
claimed but, to the best of our knowledge, this paper is the first
attempting to present quantitative evidence of it.

Method: We compare functionally-equivalent implementations of
the Gang-of-Four design patterns, developed in Java and OT/J,
using software metrics.

Results: The results of our comparison support the modularity
improvement claims made in the literature. For six of the seven
metrics used, the OT/J versions of the patterns obtained
significantly better results.

Limitations: This work uses a set of metrics originally defined
for object-oriented (OO) systems. It may be the case that the
metrics are biased, in that they were created in the context of OO
programming (OOP), before the advent of AOP. We consider this
comparison a stepping stone as, ultimately, we plan to assess the
modularity improvements with paradigm independent metrics,
which will conceivably eliminate the bias. Each individual
example from the sample used in this paper is small. In future, we
plan to replicate this experiment using larger systems, where the
benefits of AOP may be more noticeable.

Conclusion: This work contributes with evidence to fill gaps in
the body of quantitative results supporting alleged benefits to
software modularity brought by AOP languages, namely OT/J.

Requirements documents and models need to be used by many stakeholders with dierent technological prociency, during software development. Each stakeholder may need to understand the entire (or simply part of the) requirements artifacts. To empower those stakeholders, views of the requirements should be congurable to their particular needs. Information visualization techniques may help in this process. In this paper, we propose dierent views aimed at highlighting information that is relevant for a particular stakeholder, helping him to query requirements artifacts. We oer three kinds of visualization capturing language and domain elements, while providing a gradual model overview: the big picture view, the syntax-based view, and the concern-based view. We instantiate these views with i* models and introduce an implementation prototype in the iStarLab tool.

Goal-Oriented Requirements Engineering (GORE) approaches have been developed to facilitate the requirements engineers work by, for example, providing abstraction mechanisms to help eliciting and modeling requirements. One of the well-established GORE approaches is KAOS. Nevertheless, in large-scale systems building KAOS models may result in incomplete and/or complex goal models, which are difficult to understand and change. This may lead to an increase in costs of product development and evolution. Thus, for large-scale systems, the effective management of complexity and completeness of goal
models is vital. In this paper, we propose a metrics framework for supporting the quantitative assessment of complexity and completeness of KAOS goal models. Those metrics are formally specified, implemented and incorporated in a KAOS modeling tool. We validate the metrics with a set of real-world case studies and discuss the identified recurring modeling practices.