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Domain experts are increasingly employing machine learning to solve their domain-specific problems. This article presents to software engineering researchers the six key challenges that a domain expert faces in addressing their problem with a computational workflow, and the underlying executable implementation. These challenges arise out of our conceptual framework which presents the "route" of transformations that a domain expert may choose to take while developing their solution. To ground our conceptual framework in the state of the practice, this article discusses a selection of available textual and graphical workflow systems and their support for the transformations described in our framework. Example studies from the literature in various domains are also examined to highlight the tools used by the domain experts as well as a classification of the domain specificity and machine learning usage of their problem, workflow, and implementation. The state of the practice informs our discussion of the six key challenges, where we identify which challenges and transformations are not sufficiently addressed by available tools. We also suggest possible research directions for software engineering researchers to increase the automation of these tools and disseminate best-practice techniques between software engineering and various scientific domains. [ABSTRACT FROM AUTHOR]

The paper I Semi-automated metamodel/model co-evolution: a multi-level interactive approach i by Wael Kessentini and Vahid Alizadeh deals with the problem of fixing models after their metamodels have evolved, which is a time-consuming activity if performed manually. The MODELS conference series is the premier venue for model-based software and systems engineering covering all aspects of modeling, from languages and methods to tools and applications. Altogether, they are representative examples of the diversity, depth, and maturity of the current research in model-based software and systems engineering. [Extracted from the article]

We propose in this paper to consider code-smells detection as a distributed optimization problem. The idea is that different methods are combined in parallel during the optimization process to find a consensus regarding the detection of code-smells. To this end, we used Parallel Evolutionary algorithms (P-EA) where many evolutionary algorithms with different adaptations (fitness functions, solution representations, and change operators) are executed, in a parallel cooperative manner, to solve a common goal which is the detection of code-smells. An empirical evaluation to compare the implementation of our cooperative P-EA approach with random search, two single population-based approaches and two code-smells detection techniques that are not based on meta-heuristics search. The statistical analysis of the obtained results provides evidence to support the claim that cooperative P-EA is more efficient and effective than state of the art detection approaches based on a benchmark of nine large open source systems where more than 85 percent of precision and recall scores are obtained on a variety of eight different types of code-smells. [ABSTRACT FROM PUBLISHER]

Websites are an important and efficient means of communication for companies wishing to interact with their clients. Therefore, research has focused on evaluating how websites should be structured to ensure their quality. The majority of this research has focused on evaluating the quality of individual pages or that of a site as a whole. In this article, we propose the use of two-level models that combine evaluations at the page level with evaluations at the site level, and applied them to the problem of evaluating the navigability of websites. To test our models, we conducted a study with 21 subjects who had to complete navigation tasks on several websites, and compared their quality judgments to those produced by single and two-level quality models. We found that two-level models are better predictors of navigability. Finally, we show how two-level models are able to suggest modifications to improve site navigability. [ABSTRACT FROM AUTHOR]

Web-based applications are software systems that continuously evolve to meet users' needs and to adapt to new technologies. Assuring their quality is then a difficult, but essential task. In fact, a large number of factors can affect their quality. Considering these factors and their interaction involves managing uncertainty and subjectivity inherent to this kind of applications. In this article, we present a probabilistic approach for building Web quality models and the associated assessment method. The proposed approach is based on Bayesian Networks. A model is built following a four-step process consisting in collecting quality characteristics, refining them, building a model structure, and deriving the model parameters. The feasibility of the approach is illustrated on the important quality characteristic of Navigability design. To validate the produced model, we conducted an experimental study with 20 subjects and 40 web pages. The results obtained show that the scores given by the used model are strongly correlated with navigability as perceived and experienced by the users. [ABSTRACT FROM AUTHOR]

Refactoring is widely recognized as a crucial technique applied when evolving object-oriented software systems. If applied well, refactoring can improve different aspects of software quality including readability, maintainability, and extendibility. However, despite its importance and benefits, recent studies report that automated refactoring tools are underused much of the time by software developers. This paper introduces an automated approach for refactoring recommendation, called MORE, driven by 3 objectives: (1) to improve design quality (as defined by software quality metrics), (2) to fix code smells, and (3) to introduce design patterns. To this end, we adopt the recent nondominated sorting genetic algorithm, NSGA-III, to find the best trade-off between these 3 objectives. We evaluated the efficacy of our approach using a benchmark of 7 medium and large open-source systems, 7 commonly occurring code smells (god class, feature envy, data class, spaghetti code, shotgun surgery, lazy class, and long parameter list), and 4 common design pattern types (visitor, factory method, singleton, and strategy). Our approach is empirically evaluated through a quantitative and qualitative study to compare it against 3 different state-of-the art approaches, 2 popular multiobjective search algorithms, and random search. The statistical analysis of the results confirms the efficacy of our approach in improving the quality of the studied systems while successfully fixing 84% of code smells and introducing an average of 6 design patterns. In addition, the qualitative evaluation shows that most of the suggested refactorings (an average of 69%) are considered by developers to be relevant and meaningful. [ABSTRACT FROM AUTHOR]