A systematic literature review on the semi-automatic configuration of extended product lines

Product line engineering has become essential in mass customisation given its ability to reduce production costs and time to market, and to improve product quality and customer satisfaction. In product line literature, mass customisation is known as product configuration. Currently, there are multiple heterogeneous contributions in the product line configuration domain. However, a secondary study that shows an overview of the progress, trends, and gaps faced by researchers in this domain is still missing. In this context, we provide a comprehensive systematic literature review to discover which approaches exist to support the configuration process of extended product lines and how these approaches perform in practice. Extend product lines consider non-functional properties in the product line modelling. We compare and classify a total of 66 primary studies from 2000 to 2016. Mainly, we give an in-depth view of techniques used by each work, how these techniques are evaluated and their main shortcomings. As main results, our review identified (i) the need to improve the quality of the evaluation of existing approaches, (ii) a lack of hybrid solutions to support multiple configuration constraints, and (iii) a need to improve scalability and performance conditions

Gestão de produto: uma análise exploratória em equipas de trabalho multinacionais

Mestrado em Engenharia e Gestão IndustrialThis dissertation presents the results of an exploratory study conducted with the aim of identifying the key challenges in managing teams of International Product Management, that are multicultural and geographically dispersed. The work involved a preliminary literature review concerning the function of Product Manager, as well as about the functioning of global and virtual teams, followed by data collection about the role and challenges of Product Management function by means of interviews and questionnaires. The study aimed to: i) characterize Product Management function; ii) analyze the influence of organizational culture on the perception of the role of product management; iii) understand the impact of geographic dispersion of teams in professional management product; and iv) understand the implications virtual communication in product management teams.Este trabalho apresenta os resultados de um estudo exploratório conduzido com o objectivo de identificar os principais desafios na gestão de equipas de Gestão de Produto internacionais, multiculturais e geograficamente dispersas. O trabalho desenvolvido envolveu uma revisão bibliográfica preliminar sobre a posição de Gestor de Produto, equipas globais e virtuais e posteriormente a recolha de informação sobre o papel e os desafios da função de Gestão de Produto, através da condução de entrevistas e questionários. O estudo pretendeu: i) caracterizar a função da Gestão de Produto; ii) analisar a influência da cultura organizacional na percepção da função de Gestão de Produto; iii) perceber o impacto da dispersão geográfica das equipas nos profissionais de Gestão de Produto; e iv) compreender as implicações do uso de comunicação virtual nas equipas de produto

Efficient Reasoning Techniques for Large Scale Feature Models

In Software Product Lines (SPLs), a feature model can be used to represent the similarities and differences within a family of software systems. This allows describing the systems derived from the product line as a unique combination of the features in the model. What makes feature models particularly appealing is the fact that the constraints in the model prevent incompatible features from being part of the same product. Despite the benefits of feature models, constructing and maintaining these models can be a laborious task especially in product lines with a large number of features and constraints. As a result, the study of automated techniques to reason on feature models has become an important research topic in the SPL community in recent years. Two techniques, in particular, have significant appeal for researchers: SAT solvers and Binary Decision Diagrams (BDDs). Each technique has been applied successfully for over four decades now to tackle many practical combinatorial problems in various domains. Currently, several approaches have proposed the compilation of feature models to specific logic representations to enable the use of SAT solvers and BDDs. In this thesis, we argue that several critical issues related to the use of SAT solvers and BDDs have been consistently neglected. For instance, satisfiability is a well-known NP-complete problem which means that, in theory, a SAT solver might be unable to check the satisfiability of a feature model in a feasible amount of time. Similarly, it is widely known that the size of BDDs can become intractable for large models. At the same time, we currently do not know precisely whether these are real issues when feature models, especially large ones, are compiled to SAT and BDD representations. Therefore, in our research we provide a significant step forward in the state-of-the-art by examining deeply many relevant properties of the feature modeling domain and the mechanics of SAT solvers and BDDs and the sensitive issues related to these techniques when applied in that domain. Specifically, we provide more accurate explanations for the space and/or time (in)tractability of these techniques in the feature modeling domain, and enhance the algorithmic performance of these techniques for reasoning on feature models. The contributions of our work include the proposal of novel heuristics to reduce the size of BDDs compiled from feature models, several insights on the construction of efficient domain-specific reasoning algorithms for feature models, and empirical studies to evaluate the efficiency of SAT solvers in handling very large feature models

Modelling and Analysing Software Requirements and Architecture Decisions under Uncertainty

Early requirements engineering and software architectural decisions are critical to the success of software development projects. However, such decisions are confronted with complexities resulting from uncertainty about the possible impacts of decision choices on objectives; conflicting stakeholder objectives; and a huge space of alternative designs. Quantitative decision modelling is a promising approach to tackling the increasing complexity of requirements and architectural decisions. It allows one to use quantitative techniques, such as stochastic simulation and multi-objective optimisation, to model and analyse the impact of alternative decisions on stakeholders' objectives. Existing requirements and architecture methods that use quantitative decision models are limited by the difficulty of elaborating quantitative decision models and/or lack of integrated tool support for automated decision analysis under uncertainty. This thesis addresses these problems by presenting a novel modelling language and automated decision analysis technique, implemented in a tool called RADAR, intended to facilitate requirements and architecture decisions under uncertainty. RADAR's modelling language has relations to quantitative AND/OR goal models used in requirements engineering and feature models used in software product lines. The language enables modelling requirements and architectural decision problems characterised by (i) single option selection similar to mutually exclusive option selection (XOR-nodes) of feature diagrams; (ii) multiple options selection similar to non-mutually exclusive options selections (OR-nodes) of feature diagrams; and (iii) constraints dependency relationships, e.g., excludes, requires and coupling, between options of decisions. RADAR's analysis technique uses multi-objective simulation optimisation technique in evaluating and shortlisting alternatives that produces the best trade-off between stakeholders' objectives. Additionally, the analysis technique employs information value analysis to estimate the financial value of reducing uncertainty before making a decision. We evaluate RADAR's applicability, usefulness and scalability on a set of real-world systems from different application domains and characterised by design space size between 6 and 2E50. Our evaluation results show that RADAR's modelling language and analysis technique is applicable on a range of real-world requirements and architecture decision problems, and that in few seconds, RADAR can analyse decision problems characterised by large design space using highly performant optimisation method through the use of evolutionary search-based optimisation algorithms