Miguel Goulão

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.

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.

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.

KAOS is one of the most well-known goal-oriented requirements engineering approaches. Nevertheless, building large KAOS models sometimes results in incomplete and/or complex requirements models that are difficult to understand and maintain. These shortcomings often lead to an increase in costs of product development and evolution. Therefore, for large-scale systems, the ability to manage the complexity and completeness of KAOS models is essential. In this paper, we propose a metrics suite for supporting the quantitative assessment of KAOS models complexity and completeness, in order to support their early identification. We apply the metrics to an example taken from a health club system specification.

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.

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.

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.

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.

Domain-Specific Languages (DSLs) developers aim to narrow the gap between the level of abstraction used by domain users and the one provided by the DSL, in order to help taming the increased complexity of computer systems and real-world problems. The quality in use of a DSL is essential for its successful adoption. We illustrate how a usability evaluation process can be weaved into the development process of a concrete DSL - FlowSL - used for specifying humanitarian campaign processes lead by an international Non-Governmental Organization. FlowSL is being developed following an agile process using Model-Driven Development (MDD) tools, to cope with vague and poorly understood requirements in the beginning of the development process.

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 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.

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.

Domain Specific Languages (DSLs) are claimed to increase productivity, while reducing the required maintenance and programming expertise. In this context, DSL usability by domain experts is a key factor for its successful adoption. Evidence that support those improvement claims is mostly anecdotal. Our systematic literature review showed that a usability evaluation was 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 where fixing problems identified is too expensive. We argue that a systematic approach based on User Interface experimental validation techniques should be used to assess the impact of the new DSLs. The rationale is that assessing important and specially tailored usability attributes for DSLs early in language construction will ultimately foster a higher productivity of the DSL users. This paper, besides discussing the quality criteria, proposes a development and evaluation process that can be used to achieve usable DSLs in a better way.

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.