Guide to Advanced Empirical Software Engineering

By F. Shull, Janice Singer, Dag I.K. Sjøberg. 2007

906 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/24b11e369c41137e30ed026061bcfc66855e6832

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Frank F. Xu
2. Graham Neubig
3. Bogdan Vasilescu
4. Amy J. Ko
5. Margaret M. Burnett
6. Thomas D. Latoza
7. Baowen Xu
8. Xin Xia

In Information Systems

1. Pearl Brereton
2. Barbara A. Kitchenham
3. David Budgen
4. Mark Turner
5. Frank F. Xu
6. Graham Neubig
7. Margaret M. Burnett
8. Bogdan Vasilescu

Citations in the corpus

• Why we refactor? confessions of GitHub contributors (2016, result): “The achieved response rate is significantly larger than the typical 5% rate found in questionnaire-based software engineering surveys [32].”
• In-IDE Code Generation from Natural Language: Promise and Challenges (2021, methodology): “Following best practices for empirical software engineering research [104, 114], we make our study fully replicable, publishing our plugin prototype, instrumented virtual environment, the different scripts for data extraction and analysis, and the obtained anonymized raw data; see the online appendi”
• Smart Contract Development: Challenges and Opportunities (2021, methodology): “In our study, we conducted semi-structured interviews [133].”
• The role of replications in Empirical Software Engineering (2008, background): “This type of replication is classified as a conceptual replication.”
• A practical guide to controlled experiments of software engineering tools with human participants (2013, methodology): “Methodological literature instead focuses on other important but more general issues, providing conceptual background on experiments, measurement, and research design (e.g., Wohlin et al. 2000; Easterbrook et al. 2008; Shull et al. 2006; Juristo and Moreno 2001; Kitchenham et al. 2002).”
• Sampling in software engineering research: a critical review and guidelines (2020, background): “, [3, 19, 40, 46]) and sampling speci€cally for questionnaire studies [11, 30], this paper goes into signi€cantly more depth and discusses more SE-speci€c sampling problems.”
• Using Mapping Studies in Software Engineering (2008, background): “Some guidelines for this do exist (Kitchenham et al. 2002, Jedlitschka et al. 2008), but maybe we need stronger mechanisms to help encourage authors to comply with them—possibly a role that journal editors and others could perform?”
• Fair and balanced?: bias in bug-fix datasets (2009, neutral): “Shull et al. [44] illustrate how important empirical studies have become to software engineering reseach and provide a wealth of quantitative studies and methods.”