Do concern mining tools really help requirements analysts? An empirical study of the vetting process
| Autor: | J. Andres Diaz-Pace, Claudia Marcos, Alejandro Rago |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Process (engineering) 02 engineering and technology Empirical research Vetting 0502 economics and business 0202 electrical engineering electronic engineering information engineering HUMAN BEHAVIOR Software requirements USE CASE SPECIFICATIONS Otras Ciencias de la Computación e Información CROSSCUTTING CONCERN REQUIREMENTS ENGINEERING Requirements engineering 05 social sciences 020207 software engineering Data science EMPIRICAL STUDY Hardware and Architecture Ciencias de la Computación e Información TOOL SUPPORT CIENCIAS NATURALES Y EXACTAS 050203 business & management Software Natural language Information Systems |
| Zdroj: | Journal of Systems and Software. 156:181-203 |
| ISSN: | 0164-1212 |
| DOI: | 10.1016/j.jss.2019.06.073 |
| Popis: | Software requirements are often described in natural language because they are useful to communicate and validate. Due to their focus on particular facets of a system, this kind of specifications tends to keep relevant concerns (also known as early aspects) from the analysts’ view. These concerns are known as crosscutting concerns because they appear scattered among documents. Concern mining tools can help analysts to uncover concerns latent in the text and bring them to their attention. Nonetheless, analysts are responsible for vetting tool-generated solutions, because the detection of concerns is currently far from perfect. In this article, we empirically investigate the role of analysts in the concern vetting process, which has been little studied in the literature. In particular, we report on the behavior and performance of 55 subjects in three case-studies working with solutions produced by two different tools, assessed in terms of binary classification measures. We discovered that analysts can improve “bad” solutions to a great extent, but performed significantly better with “good” solutions. We also noticed that the vetting time is not a decisive factor to their final accuracy. Finally, we observed that subjects working with solutions substantially different from those of existing tools (better recall) can also achieve a good performance. Fil: Rago, Alejandro Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto de Investigación en Ciencias de La Salud; Argentina Fil: Diaz Pace, Jorge Andres. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto de Investigación en Ciencias de La Salud; Argentina Fil: Marcos, Claudia Andrea. Universidad Nacional del Centro de la Provincia de Buenos Aires; Argentina. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect