A Survey-Based Qualitative Study to Characterize Expectations of Software Developers from Five Stakeholders
Not on the map. Why?
Abstract (arXiv)
Background: Studies on developer productivity and well-being find that the perceptions of productivity in a software team can be a socio-technical problem. Intuitively, problems and challenges can be better handled by managing expectations in software teams. Aim: Our goal is to understand whether the expectations of software developers vary towards diverse stakeholders in software teams. Method: We surveyed 181 professional software developers to understand their expectations from five different stakeholders: (1) organizations, (2) managers, (3) peers, (4) new hires, and (5) government and educational institutions. The five stakeholders are determined by conducting semi-formal interviews of software developers. We ask open-ended survey questions and analyze the responses using open coding. Results: We observed 18 multi-faceted expectations types. While some expectations are more specific to a stakeholder, other expectations are cross-cutting. For example, developers expect work-benefits from their organizations, but expect the adoption of standard software engineering (SE) practices from their organizations, peers, and new hires. Conclusion: Out of the 18 categories, three categories are related to career growth. This observation supports previous research that happiness cannot be assured by simply offering more money or a promotion. Among the most number of responses, we find expectations from educational institutions to offer relevant teaching and from governments to improve job stability, which indicate the increasingly important roles of these organizations to help software developers. This observation can be especially true during the COVID-19 pandemic.
Key facts. Cited by 4 papers in the map (data through 13 Oct 2026). First posted on arXiv 20 Jul 2021.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2024: 1; 2025: 3.
4 papers.
- 2024A Comparative Analysis of Transformer and LSTM Models for Detecting Suicidal Ideation on Redditcited by 7
- 2025Advancing Mental Disorder Detection: A Comparative Evaluation of Transformer and LSTM Architectures on Social Mediacited by 2
- 2025Beyond Architectures: Evaluating the Role of Contextual Embeddings in Detecting Bipolar Disorder on Social Mediacited by 1
- 2025Mental Multi-class Classification on Social Media: Benchmarking Transformer Architectures against LSTM Modelscited by 1
- 2025Mental Multi-class Classification on Social Media: Benchmarking Transformer Architectures against LSTM Models20 Sep 2025
- 2025Advancing Mental Disorder Detection: A Comparative Evaluation of Transformer and LSTM Architectures on Social Media17 Jul 2025
- 2025Beyond Architectures: Evaluating the Role of Contextual Embeddings in Detecting Bipolar Disorder on Social Media17 Jul 2025
- 2024A Comparative Analysis of Transformer and LSTM Models for Detecting Suicidal Ideation on Reddit23 Nov 2024