SC Harvester Papers Database Interface

Conclusions

M. Staron. In: Action Research in Software Engineering. 2019

Specifying Learning

M. Staron. In: Action Research in Software Engineering. 2019

Evaluation

M. Staron. In: Action Research in Software Engineering. 2019

Action Planning

M. Staron. In: Action Research in Software Engineering. 2019

Introduction

M. Staron. In: Action Research in Software Engineering. 2019

Action Research in Software Engineering: Theory and Applications

M. Staron. In: Action Research in Software Engineering. 2019

Choice Blindness, Confabulatory Introspection, and Obsessive–Compulsive Symptoms: Investigation in a Clinical Sample

S. Wong, F. Aardema, Martha Giraldo‐O'Meara, Lars Hall, Petter Johansson. In: Cognitive Therapy and Research. 2019

From Efficiency to Effectiveness: Delivering Business Value Through Software

J. Bosch. In: . 2019

Customer Churn Prediction in B2B Contexts

Iris Figalist, Christoph Elsner, J. Bosch, H. Olsson. In: . 2019

API Management Challenges in Ecosystems

Sebastien Andreo, J. Bosch. In: . 2019

Dynamic Data Management for Machine Learning in Embedded Systems: A Case Study

Hamza Ouhaichi, H. Olsson, J. Bosch. In: . 2019

Leveraging Business Transformation with Machine Learning Experiments

D. I. Mattos, J. Bosch, H. Olsson. In: . 2019

Continuous Data-driven Software Engineering - Towards a Research Agenda

I. Gerostathopoulos, M. Konersmann, Stephan Krusche, D. I. Mattos, J. Bosch et al. In: ACM SIGSOFT Software Engineering Notes. 2019

Abstract: The rapid pace with which software needs to be built, together with the increasing need to evaluate changes for end users both quantitatively and qualitatively calls for novel software engineering approaches that focus on short release cycles, continuous deployment and delivery, experiment-driven feature development, feedback from users, and rapid tool-assisted feedback to developers. To realize t...

SST'19 - Software and Systems Traceability

J. Steghöfer, Nan Niu, Jin L. C. Guo, Anas Mahmoud. In: ACM SIGSOFT Software Engineering Notes. 2019

Abstract: Traceability is the ability to relate di erent artifacts during the development and operation of a system to each other. It enables program comprehension, change impact analysis, and facilitates the cooperation of engineers from di erent disciplines. The 10th International Workshop on Software and Systems Traceability (former International Workshop on Traceability in Emerging Forms of Software Eng...

Recognizing lines of code violating company-specific coding guidelines using machine learning

Miroslaw Ochodek, R. Hebig, Wilhelm Meding, Gert Frost, M. Staron. In: Empirical Software Engineering. 2019