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Value-based Software Engineering: A Systematic Mapping Study

Salleh Norsaremah, Mendes Emilia, Mendes Fabiana, Lekamlage Charitha Dissanayake, Petersen Kai

e-Informatica Software Engineering Journal

2023

Vol. 17, nr 1

art. no. 230106

Background: Integrating value-oriented perspectives into the principles and practices of software engineering is fundamental to ensure that software development activities address key stakeholders’ views and also balance short-and long-term goals. This is put forward in the discipline of value-based software engineering (VBSE) Aim: This study aims to provide an overview of VBSE with respect to the research efforts that have been put into VBSE. Method: We conducted a systematic mapping study to classify evidence on value definitions, studies’ quality, VBSE principles and practices, research topics, methods, types, contribution facets, and publication venues. Results: From 143 studies we found that the term “value” has not been clearly defined in many studies. VB Requirements Engineering and VB Planning and Control were the two principles mostly investigated, whereas VB Risk Management and VB People Management were the least researched. Most studies showed very good reporting and relevance quality, acceptable credibility, but poor in rigour. Main research topic was Software Requirements and case study research was the method used the most. The majority of studies contribute towards methods and processes, while very few studies have proposed metrics and tools.Conclusion: We highlighted the research gaps and implications for research and practice to support VBSE.

A Comparison of Citation Sources for Reference and Citation-Based Search in Systematic Literature Reviews

bin Ali Nauman, Tanveer Binish

e-Informatica Software Engineering Journal

2022

Vol. 16, nr 1

art. no. 220106

Context: In software engineering, snowball sampling has been used as a supplementary and primary search strategy. The current guidelines recommend using Google Scholar (GS) for snowball sampling. However, the use of GS presents several challenges when using it as a source for citations and references. Objective: To compare the effectiveness and usefulness of two leading citation databases (GS and Scopus) for use in snowball sampling search. Method: We relied on a published study that has used snowball sampling as a search strategy and GS as the citation source. We used its primary studies to compute precision and recall for Scopus. Results: In this particular case, Scopus was highly effective with 95% recall and had better precision of 5.1% compared to GS’s 2.8%. Moreover, Scopus found nine additional relevant papers. On average, one would read approximately 15 extra papers in GS than Scopus to identify one additional relevant paper. Furthermore, Scopus supports batch downloading of both citations and papers’ references, has better quality metadata, and does better source filtering. Conclusion: This study suggests that Scopus seems to be more effective and useful for snowball sampling than GS for systematic secondary studies attempting to identify peer-reviewed literature.