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- Garousi, Vahid, et al.
(författare)
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Aligning software engineering education with industrial needs : A meta-analysis
- 2019
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Ingår i: Journal of Systems and Software. - : Elsevier Inc.. - 0164-1212 .- 1873-1228. ; 156, s. 65-83
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Tidskriftsartikel (refereegranskat)abstract
- Context: According to various reports, many software engineering (SE) graduates often face difficulties when beginning their careers, which is mainly due to misalignment of the skills learned in university education with what is needed in the software industry. Objective: Our objective is to perform a meta-analysis to aggregate the results of the studies published in this area to provide a consolidated view on how to align SE education with industry needs, to identify the most important skills and also existing knowledge gaps. Method: To synthesize the body of knowledge, we performed a systematic literature review (SLR), in which we systematically selected a pool of 35 studies and then conducted a meta-analysis using data extracted from those studies. Results: Via a meta-analysis and using data from 13 countries and over 4,000 data points, highlights of the SLR include: (1) software requirements, design, and testing are the most important skills; and (2) the greatest knowledge gaps are in configuration management, SE models and methods, SE process, design (and architecture), as well as in testing. Conclusion: This paper provides implications for both educators and hiring managers by listing the most important SE skills and the knowledge gaps in the industry. © 2019 Elsevier Inc.
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| 2. |
- Garousi, Vahid, et al.
(författare)
-
Closing the Gap Between Software Engineering Education and Industrial Needs
- 2020
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Ingår i: IEEE Software. - : IEEE Computer Society. - 0740-7459 .- 1937-4194. ; 7:2, s. 68-77
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Tidskriftsartikel (refereegranskat)abstract
- According to different reports, many recent software engineering graduates often face difficulties when beginning their professional careers, due to misalignment of the skills learnt in their university education with what is needed in industry. To address that need, many studies have been conducted to align software engineering education with industry needs. To synthesize that body of knowledge, we present in this paper a systematic literature review (SLR) which summarizes the findings of 33 studies in this area. By doing a meta-analysis of all those studies and using data from 12 countries and over 4,000 data points, this study will enable educators and hiring managers to adapt their education / hiring efforts to best prepare the software engineering workforce. IEEE
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| 3. |
- Zimelewicz, Eduardo, et al.
(författare)
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ML-Enabled Systems Model Deployment and Monitoring : Status Quo and Problems
- 2024
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Ingår i: Software Quality as a Foundation for Security. - : Springer Science+Business Media B.V.. - 9783031562808 ; , s. 112-131
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Konferensbidrag (refereegranskat)abstract
- Systems that incorporate Machine Learning (ML) models, often referred to as ML-enabled systems, have become commonplace. However, empirical evidence on how ML-enabled systems are engineered in practice is still limited; this is especially true for activities surrounding ML model dissemination. [Goal] We investigate contemporary industrial practices and problems related to ML model dissemination, focusing on the model deployment and the monitoring ML life cycle phases. [Method] We conducted an international survey to gather practitioner insights on how ML-enabled systems are engineered. We gathered a total of 188 complete responses from 25 countries. We analyze the status quo and problems reported for the model deployment and monitoring phases. We analyzed contemporary practices using bootstrapping with confidence intervals and conducted qualitative analyses on the reported problems applying open and axial coding procedures. [Results] Practitioners perceive the model deployment and monitoring phases as relevant and difficult. With respect to model deployment, models are typically deployed as separate services, with limited adoption of MLOps principles. Reported problems include difficulties in designing the architecture of the infrastructure for production deployment and legacy application integration. Concerning model monitoring, many models in production are not monitored. The main monitored aspects are inputs, outputs, and decisions. Reported problems involve the absence of monitoring practices, the need to create custom monitoring tools, and the selection of suitable metrics. [Conclusion] Our results help provide a better understanding of the adopted practices and problems in practice and support guiding ML deployment and monitoring research in a problem-driven manner. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
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