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

Characterizing industry-academia collaborations in software engineering: evidence from 101 projects

Research collaboration between industry and academia supports improvement and innovation in industry and helps ensure the industrial relevance of academic research. However, many researchers and practitioners in the community believe that the level of joint industry-academia collaboration (IAC) projects in Software Engineering (SE) research is relatively low, creating a barrier between research and practice. The goal of the empirical study reported in this paper is to explore and characterize the state of IAC with respect to industrial needs, developed solutions, impacts of the projects and also a set of challenges, patterns and anti-patterns identified by a recent Systematic Literature Review (SLR) study. To address the above goal, we conducted an opinion survey among researchers and practitioners with respect to their experience in IAC. Our dataset includes 101 data points from IAC projects conducted in 21 different countries. Our findings include: (1) the most popular topics of the IAC projects, in the dataset, are: software testing, quality, process, and project managements; (2) over 90% of IAC projects result in at least one publication; (3) almost 50% of IACs are initiated by industry, busting the myth that industry tends to avoid IACs; and (4) 61% of the IAC projects report having a positive impact on their industrial context, while 31% report no noticeable impacts or were “not sure”. To improve this situation, we present evidence-based recommendations to increase the success of IAC projects, such as the importance of testing pilot solutions before using them in industry. This study aims to contribute to the body of evidence in the area of IAC, and benefit researchers and practitioners. Using the data and evidence presented in this paper, they can conduct more successful IAC projects in SE by being aware of the challenges and how to overcome them, by applying best practices (patterns), and by preventing anti-patterns.The authors would like to thank the researchers and practitioners who participated in this survey. João M. Fernandes was supported by FCT (Fundação para a Ciência e Tecnologia) within the Project Scope UID/CEC/00319/2013. Dietmar Pfahl was supported by the institutional research grant IUT20-55 of the Estonian Research Council. Andrea Arcuri was supported by the Research Council of Norway (grant agreement No 274385). Mika Mäntylä was partially supported by Academy of Finland grant and ITEA3 / TEKES grant

DASC-PM v1.0 : ein Vorgehensmodell für Data-Science-Projekte

Das Thema Data Science hat in den letzten Jahren in vielen Organisationen stark an Aufmerksamkeit gewonnen. Häufig herrscht jedoch weiterhin große Unklarheit darüber, wie diese Disziplin von anderen abzugrenzen ist, welche Besonderheiten der Ablauf eines Data-Science-Projekts besitzt und welche Kompetenzen vorhanden sein müssen, um ein solches Projekt durchzuführen. In der Hoffnung, einen kleinen Beitrag zur Beseitigung dieser Unklarheiten leisten zu können, haben wir von April 2019 bis Februar 2020 in einer offenen und virtuellen Arbeitsgruppe mit Vertretern aus Theorie und Praxis das vorliegende Dokument erarbeitet, in dem ein Vorgehensmodell für Data-Science-Projekte beschrieben wird – das Data Science Process Model (DASC-PM). Ziel war es dabei nicht, neue Herangehensweisen zu entwickeln, sondern viel-mehr, vorhandenes Wissen zusammenzutragen und in geeigneter Form zu strukturieren. Die Ausarbeitung ist als Zusammenführung der Erfahrung sämtlicher Teilnehmerinnen und Teilnehmer dieser Arbeitsgruppe zu verstehen

What is software quality for AI engineers?:towards a thinning of the fog

Abstract It is often overseen that AI-enabled systems are also software systems and therefore rely on software quality assurance (SQA). Thus, the goal of this study is to investigate the software quality assurance strategies adopted during the development, integration, and maintenance of AI/ML components and code. We conducted semi-structured interviews with representatives of ten Austrian SMEs that develop AI-enabled systems. A qualitative analysis of the interview data identified 12 issues in the development of AI/ML components. Furthermore, we identified when quality issues arise in AI/ML components and how they are detected. The results of this study should guide future work on software quality assurance processes and techniques for AI/ML components

Ground truth deficiencies in software engineering:when codifying the past can be counterproductive

Abstract Many software engineering tools build and evaluate their models based on historical data to support development and process decisions. These models help us answer numerous interesting questions, but have their own caveats. In a real-life setting, the objective function of human decision-makers for a given task might be influenced by a whole host of factors that stem from their cognitive biases, subverting the ideal objective function required for an optimally functioning system. Relying on this data as ground truth may give rise to systems that end up automating software engineering decisions by mimicking past sub-optimal behaviour. We illustrate this phenomenon and suggest mitigation strategies to raise awareness

Prognose der österreichischen Wirtschaft 1998/99: Jahresmodell LIMA/97 ; Ökonometrisches Forschungsprogramm des Instituts für Höhere Studien

aus dem Inhaltsverzeichnis: Einleitung und Zusammenfassung; Die internationale Konjunktur; Die österreichische Außenwirtschaft; Perspektiven der Inlandskonjunktur; Monetäre Prognose