Comparing manual and automated feature location in conceptual models: A Controlled experiment

• Autores: Francisca Pérez, Jorge Echeverría Ochoa, Raúl Lapeña, Carlos Cetina Englada
• Localización: Actas de las XXV Jornadas de Ingeniería del Software y Bases de Datos (JISBD 2021): [Málaga, 22 al 24 de septiembre de 2021] / coord. por Rafael Capilla Sevilla, Maider Azanza Sese, Miguel Rodríguez Luaces, M. M. Roldán García, Dolores Burgueño Caballero, José Raúl Romero Salguero, José Antonio Parejo Maestre, José Francisco Chicano García, Marcela Genero, Óscar Díaz García, José González Enríquez, María Carmen Penades Gramage; Silvia Mara Abrahao Gonzales (col.), 2021
• Idioma: inglés
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• Resumen

  • Context: Maintenance activities cannot be completed without locating the set of software artifacts that realize a particular feature of a software system. Manual Feature Location (FL) is widely used in industry, but it becomes challenging (time-consuming and error prone) in large software repositories. To reduce manual efforts, automated FL techniques have been proposed. Research efforts in FL tend to make comparisons between automated FL techniques, ignoring manual FL techniques. Moreover, existing research puts the focus on code, neglecting other artifacts such as models. Objective: This paper aims to compare manual FL against automated FL in models to answer important questions about performance, productivity, and satisfaction of both treatments. Method: We run an experiment for comparing manual and automated FL on a set of 18 subjects (5 experts and 13 non-experts) in the domain of our industrial partner, BSH, manufacturer of induction hobs for more than 15 years. We measure performance (recall, precision, and F-measure), productivity (ratio between F-measure and spent time), and satisfaction (perceived ease of use, perceived usefulness, and intention to use) of both treatments, and perform statistical tests to assess whether the obtained differences are significant. Results: Regarding performance, manual FL significantly outperforms automated FL in precision and F-measure (up to 27.79+ACU and 19.05+ACU, respectively), whereas automated FL significantly outperforms manual FL in recall (up to 32.18+ACU). Regarding productivity, manual FL obtains 3.43+ACU-/min, which improves automated FL significantly. Finally, there are no significant differences in satisfaction for both treatments. Conclusions: The findings of our work can be leveraged to advance research to improve the results of manual and automated FL techniques. For instance, automated FL in industry faces issues such as low discrimination capacity. In addition, the obtained satisfaction results have implications for the usage and possible combination of manual, automated, and guided FL techniques.