Beyond data mining; towards "idea engineering"

Abstract—SE data mining tools can be reconfigured to define and explore the space of decisions made by a community. Index Terms—Data mining, software engineering, artificial intelligenc

Evolutionary Search Techniques with Strong Heuristics for Multi-Objective Feature Selection in Software Product Lines

Software design is a process of trading off competing objectives. If the user objective space is rich, then we should use optimizers that can fully exploit that richness. For example, this study configures software product lines (expressed as feature models) using various search-based software engineering methods. Our main result is that as we increase the number of optimization objectives, the methods in widespread use (e.g. NSGA-II, SPEA2) perform much worse than IBEA (Indicator-Based Evolutionary Algorithm). IBEA works best since it makes most use of user preference knowledge. Hence it does better on the standard measures (hypervolume and spread) but it also generates far more products with 0 violations of domain constraints. We also present significant improvements to IBEA\u27s performance by employing three strong heuristic techniques that we call PUSH, PULL, and seeding. The PUSH technique forces the evolutionary search to respect certain rules and dependencies defined by the feature models, while the PULL technique gives higher weight to constraint satisfaction as an optimization objective and thus achieves a higher percentage of fully-compliant configurations within shorter runtimes. The seeding technique helps in guiding very large feature models to correct configurations very early in the optimization process. Our conclusion is that the methods we apply in search-based software engineering need to be carefully chosen, particularly when studying complex decision spaces with many optimization objectives. Also, we conclude that search methods must be customized to fit the problem at hand. Specifically, the evolutionary search must respect domain constraints

A influência de fatores na produtividade do desenvolvimento de software de acordo com um modelo de estruturas teóricas

This work presents an evidence-based model describing the effects of a set of factors on software development productivity, obtained through an evidence synthesis method in Software Engineering. Thus, the relationships among this set and the software development productivity (observed phenomena) are described as results of combining theoretical structures capable of expressing and dealing with differences between different effects and uncertainties varying according to the types of studies found in the literature. Besides, to evaluate the model found, its findings are confronted with a survey capturing the practitioners’ perception (managers and leaders of software projects in Brazilian organizations). The degree of agreement between research (the model) and practice (the practitioners’ perception) shows that scientific knowledge does not differ considerably from the reality experienced by software projects when both of them refer to the influence of factors on software development productivity. The impression that research and practice on the theme go through different paths persists. According to this work, the reasons for this impression are more related to the use of non-standardized and, perhaps, inappropriate measures used to perceive and monitor the influence of factors as well as to measure the software development productivityEste trabalho apresenta um modelo baseado em evidências que descreve efeitos de alguns fatores na produtividade do desenvolvimento de software, obtidos através de um método de síntese de evidências em Engenharia de Software. Deste modo, as relações entre um conjunto de fatores e a produtividade do desenvolvimento de software (fenômenos observados) são descritas como resultados da combinação de estruturas teóricas capazes de expressar e tratar diferenças entre efeitos e incertezas variadas de acordo com os tipos de estudos primários encontrados na literatura. Além disso, para avaliar o modelo encontrado, seus achados são confrontados com uma pesquisa de opinião realizada para capturar a percepção de profissionais da prática (gestores e líderes de projetos de software em organizações brasileiras). O grau de concordância entre a pesquisa (o modelo) e a prática (a percepção dos profissionais) demonstra que, aparentemente, o conhecimento científico não diverge consideravelmente da realidade vivenciada pelos projetos de software no Brasil, quando ambos se referem à influência de fatores na produtividade do desenvolvimento de software. Persiste a impressão, entretanto, de que a pesquisa e a prática no tema percorrem caminhos distintos. De acordo com este trabalho, a impressão do distanciamento parece estar relacionadas à questão do uso de medidas não-padronizadas e, talvez, inapropriadas para mensurar os fatores e a produtividade do desenvolvimento de softwar

Multiobjective simulation optimisation in software project management

Traditionally, simulation has been used by project managers in optimising decision making. However, current simulation packages only include simulation optimisation which considers a single objective (or multiple objectives combined into a single fitness function). This paper aims to describe an approach that consists of using multiobjective optimisation techniques via simulation in order to help software project managers find the best values for initial team size and schedule estimates for a given project so that cost, time and productivity are optimised. Using a System Dynamics (SD) simulation model of a software project, the sensitivity of the output variables regarding productivity, cost and schedule using different initial team size and schedule estimations is determined. The generated data is combined with a well-known multiobjective optimisation algorithm, NSGA-II, to find optimal solutions for the output variables. The NSGA-II algorithm was able to quickly converge to a set of optimal solutions composed of multiple and conflicting variables from a medium size software project simulation model. Multiobjective optimisation and SD simulation modeling are complementary techniques that can generate the Pareto front needed by project managers for decision making. Furthermore, visual representations of such solutions are intuitive and can help project managers in their decision making process. Part of this work was carried out while visiting Oxfor