Introduction to the Special Issue on Software Engineering Methods, Tools and Products Improvement and Evaluation

• Autorzy: Madeyski L. , Ochodek M.

Identyfikatory

DOI

10.1515/fcds-2018-0013

• Języki publikacji: EN
• Wydawca: Wydawnictwo Politechniki Poznańskiej
• Czasopismo: Foundations of Computing and Decision Sciences
• Rocznik: 2018
• Tom: Vol. 43, No. 4
• Strony: 247--250
• Opis fizyczny: Bibliogr. 11 poz.

Twórcy

autor

Madeyski L.

• Wroclaw University of Science and Technology, Faculty of Computer Science and Management, Wybrzeze Wyspianskiego 27, Wroclaw, Poland

autor

Ochodek M.

• Poznan University of Technology, Faculty of Computing, ul. Piotrowo 2, Poznań 60-965, Poland

Bibliografia

• [1] ISBSG Repository Data Release 11. International Software Benchmarking Standards Group, 2009.
• [2] OWL 2 Web Ontology Language. Document Overview (Second Edition), W3C, 11 December 2012, 2009.
• [3] Kassab M. An empirical study on the requirements engineering practices for agile software development. In Software Engineering and Advanced Applications (SEAA), 2014 40th EUROMICRO Conference on, pages 254–261. IEEE, 2014.
• [4] Kowalska J. and Ochodek M. Supporting analogy-based effort estimation with the use of ontologies. e-Informatica Software Engineering Journal, 8(1):53–64, 2014.
• [5] MIT Technology Review. Machine Learning: The New Proving Ground For Competitive Advantage. https://www.technologyreview.com/s/603872/machine-learning-the-new-proving-ground-for-competitive-advantage/. Accessed: 2017-09-03.
• [6] Pettey C. Gartner says worldwide software market grew 4.8 percent in 2013. Gartner, Stamford, Connecticut, 2014.
• [7] Sadowska M. and Huzar Z. Representation of uml class diagrams in owl 2 on the background of domain ontologies. e-Informatica Software Engineering Journal, 13(1):63–104, 2019.
• [8] Shahin M., Babar M. A., and Zhu L. Continuous integration, delivery and deployment: a systematic review on approaches, tools, challenges and practices. IEEE Access, 5:3909–3943, 2017.
• [9] Shepperd M., Bowes D., and Hall T. Researcher Bias: The Use of Machine Learning in Software Defect Prediction. IEEE Transactions in Software Engineering, 40(6):603–616, 2014.
• [10] Unterkalmsteiner M., Abrahamsson P., Wang X., Nguyen-Duc A., Shah S., Bajwa S. S., Baltes G. H., Conboy K., Cullina E., Dennehy D., Edison H., Fernandez-Sanchez C., Garbajosa J., Gorschek T., Klotins E., Hokkanen L., Kon F., Lunesu I., Marchesi M., Morgan L., Oivo M., Selig C., Seppänen P., Sweetman R., Tyrväinen P., Ungerer C., and Yagüe A. Software startups – a research agenda. e-Informatica Software Engineering Journal, 10(1):89–124, 2016.
• [11] Usman M., Mendes E., and Börstler J. Effort estimation in agile software development: A survey on the state of the practice. In Proceedings of the 19th International Conference on Evaluation and Assessment in Software Engineering, page 12. ACM, 2015.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).