Reporting Statistical Validity and Model Complexity in Machine Learning based Computational Studies

Background:: Statistical validity and model complexity are both important concepts to enhanced understanding and correctness assessment of computational models. However, information about these are often missing from publications applying machine learning. Aim: The aim of this study is to show the importance of providing details that can indicate statistical validity and complexity of models in publications. This is explored in the context of citation screening automation using machine learning techniques. Method: We built 15 Support Vector Machine (SVM) models, each developed using word2vec (average word) features --- and data for 15 review topics from the Drug Evaluation Review Program (DERP) of the Agency for Healthcare Research and Quality (AHRQ). Results: The word2vec features were found to be sufficiently linearly separable by the SVM and consequently we used the linear kernels. In 11 of the 15 models, the negative (majority) class used over 80% of its training data as support vectors (SVs) and approximately 45% of the positive training data. Conclusions: In this context, exploring the SVs revealed that the models are overly complex against ideal expectations of not more than 2%-5% (and preferably much less) of the training vectors

Industry-academia collaborations in software engineering: An empirical analysis of challenges, patterns and anti-patterns in research projects

Research collaboration between industry and academia supports improvement and innovation in industry and helps to ensure industrial relevance in academic research. However, many researchers and practitioners believe that the level of joint industry-academia collaboration (IAC) in software engineering (SE) research is still relatively low, compared to the amount of activity in each of the two communities. The goal of the empirical study reported in this paper is to exploratory characterize the state of IAC with respect to a set of challenges, patterns and anti-patterns identified by a recent Systematic Literature Review study. To address the above goal, we gathered the opinions of researchers and practitioners w.r.t. their experiences in IAC projects. Our dataset includes 47 opinion data points related to a large set of projects conducted in 10 different countries. We aim to contribute to the body of evidence in the area of IAC, for the benefit of researchers and practitioners in conducting future successful IAC projects in SE. As an output, the study presents a set of empirical findings and evidence-based recommendations to increase the success of IAC projects.Supported by the National Research Fund, Luxembourg FNR/P10/03. Supported by FCT (Fundação para a Ciˆencia e Tecnologia) within the Project Scope UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

On the Unhappiness of Software Developers

The happy-productive worker thesis states that happy workers are more productive. Recent research in software engineering supports the thesis, and the ideal of flourishing happiness among software developers is often expressed among industry practitioners. However, the literature suggests that a cost-effective way to foster happiness and productivity among workers could be to limit unhappiness. Psychological disorders such as job burnout and anxiety could also be reduced by limiting the negative experiences of software developers. Simultaneously, a baseline assessment of (un)happiness and knowledge about how developers experience it are missing. In this paper, we broaden the understanding of unhappiness among software developers in terms of (1) the software developer population distribution of (un)happiness, and (2) the causes of unhappiness while developing software. We conducted a large-scale quantitative and qualitative survey, incorporating a psychometrically validated instrument for measuring (un)happiness, with 2220 developers, yielding a rich and balanced sample of 1318 complete responses. Our results indicate that software developers are a slightly happy population, but the need for limiting the unhappiness of developers remains. We also identified 219 factors representing causes of unhappiness while developing software. Our results, which are available as open data, can act as guidelines for practitioners in management positions and developers in general for fostering happiness on the job. We suggest considering happiness in future studies of both human and technical aspects in software engineering.Peer reviewe