Measuring Software Process: A Systematic Mapping Study

Context: Measurement is essential to reach predictable performance and high capability processes. It provides support for better understanding, evaluation, management, and control of the development process and project, as well as the resulting product. It also enables organizations to improve and predict its process’s performance, which places organizations in better positions to make appropriate decisions. Objective: This study aims to understand the measurement of the software development process, to identify studies, create a classification scheme based on the identified studies, and then to map such studies into the scheme to answer the research questions. Method: Systematic mapping is the selected research methodology for this study. Results: A total of 462 studies are included and classified into four topics with respect to their focus and into three groups based on the publishing date. Five abstractions and 64 attributes were identified, 25 methods/models and 17 contexts were distinguished. Conclusion: capability and performance were the most measured process attributes, while effort and performance were the most measured project attributes. Goal Question Metric and Capability Maturity Model Integration were the main methods and models used in the studies, whereas agile/lean development and small/medium-size enterprise were the most frequently identified research contexts.Ministerio de Economía y Competitividad TIN2013-46928-C3-3-RMinisterio de Economía y Competitividad TIN2016-76956-C3-2- RMinisterio de Economía y Competitividad TIN2015-71938-RED

Bioinformatics tools in predictive ecology: Applications to fisheries

This article is made available throught the Brunel Open Access Publishing Fund - Copygith @ 2012 Tucker et al.There has been a huge effort in the advancement of analytical techniques for molecular biological data over the past decade. This has led to many novel algorithms that are specialized to deal with data associated with biological phenomena, such as gene expression and protein interactions. In contrast, ecological data analysis has remained focused to some degree on off-the-shelf statistical techniques though this is starting to change with the adoption of state-of-the-art methods, where few assumptions can be made about the data and a more explorative approach is required, for example, through the use of Bayesian networks. In this paper, some novel bioinformatics tools for microarray data are discussed along with their ‘crossover potential’ with an application to fisheries data. In particular, a focus is made on the development of models that identify functionally equivalent species in different fish communities with the aim of predicting functional collapse

Improved risk analysis for large projects: Bayesian networks approach

PhDGenerally risk is seen as an abstract concept which is difficult to measure. In this thesis, we consider quantification in the broader sense by measuring risk in the context of large projects. By improved risk measurement, it may be possible to identify and control risks in such a way that the project is completed successfully in spite of the risks. This thesis considers the trade-offs that may be made in project risk management, specifically time, cost and quality. The main objective is to provide a model which addresses the real problems and questions that project managers encounter, such as: • If I can afford only minimal resources, how much quality is it possible to achieve? • What resources do I need in order to achieve the highest quality possible? • If I have limited resources and I want the highest quality, how much functionality do I need to lose? We propose the use of a causal risk framework that is an improvement on the traditional modelling approaches, such as the risk register approach, and therefore contributes to better decision making. The approach is based on Bayesian Networks (BNs). BNs provide a framework for causal modelling and offer a potential solution to some of the classical modelling problems. Researchers have recently attempted to build BN models that incorporate relationships between time, cost, quality, functionality and various process variables. This thesis analyses such BN models and as part of a new validation study identifies their strengths and weaknesses. BNs have shown considerable promise in addressing the aforementioned problems, but previous BN models have not directly solved the trade-off problem. Major weaknesses are that they do not allow sensible risk event measurement and they do not allow full trade-off analysis. The main hypothesis is that it is possible to build BN models that overcome these limitations without compromising their basic philosophy

International conference on software engineering and knowledge engineering: Session chair

The Thirtieth International Conference on Software Engineering and Knowledge Engineering (SEKE 2018) will be held at the Hotel Pullman, San Francisco Bay, USA, from July 1 to July 3, 2018. SEKE2018 will also be dedicated in memory of Professor Lofti Zadeh, a great scholar, pioneer and leader in fuzzy sets theory and soft computing. The conference aims at bringing together experts in software engineering and knowledge engineering to discuss on relevant results in either software engineering or knowledge engineering or both. Special emphasis will be put on the transference of methods between both domains. The theme this year is soft computing in software engineering & knowledge engineering. Submission of papers and demos are both welcome

Research and Education in Computational Science and Engineering

Over the past two decades the field of computational science and engineering (CSE) has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the scientific and engineering workforce. Informed by centuries of theory and experiment, CSE performs computational experiments to answer questions that neither theory nor experiment alone is equipped to answer. CSE provides scientists and engineers of all persuasions with algorithmic inventions and software systems that transcend disciplines and scales. Carried on a wave of digital technology, CSE brings the power of parallelism to bear on troves of data. Mathematics-based advanced computing has become a prevalent means of discovery and innovation in essentially all areas of science, engineering, technology, and society; and the CSE community is at the core of this transformation. However, a combination of disruptive developments---including the architectural complexity of extreme-scale computing, the data revolution that engulfs the planet, and the specialization required to follow the applications to new frontiers---is redefining the scope and reach of the CSE endeavor. This report describes the rapid expansion of CSE and the challenges to sustaining its bold advances. The report also presents strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie

Gender stereotyping and wage discrimination among Italian graduates

This paper addresses the gender pay gap among Italian university graduates on entry to the labour market and stresses the importance of gender stereotypes on subjective assessment of individual productivity. Our data show that in contexts where the stereotype is most likely to occur, the unexplained component of the gender pay gap is higher. Moreover, we find evidence that being excellent at school does not ensures that a woman will be rewarded as an equivalently performing man, but serves to counteract the gender bias in on-the-job evaluations