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

Evaluating the influence of scope on feature location

“…Feature location in models at the industrial scale is a central topic in previous works from our SVIT research group [16,17,18,19,20,21,22,23,24]. Given a feature description as input, these works [16,17,18] rank the model fragments that are relevant for the feature and explore different approaches to guide the automated feature localization: clustering (through Formal Concept Analysis) [16], empirical learning (through Learning to Rank) [17], and combinations of Similitude, Understandability, and Timing (through Latent Semantic Indexing, Model Size, and Defect Principle, respectively) [18].…”

Variability management

“…We consider the challenge as a feature location problem [2] comprises of three tasks, that is feature identification, feature location, and feature mapping [14]. The manual approach in feature location is commonly used in the industry [18] and has been applied in a different domain, e.g., [8]. Since the case study was taken from industrial cases, we prefer the manual approach that is commonly used in the industry.…”

Empirical Formal Methods: Guidelines for Performing Empirical Studies on Formal Methods

“…When the focus is human factors, e.g., competence or domain knowledge, a representative work is the one by Aranda et al [73], on the effect of domain knowledge on elicitation activities. Finally a comparison between an automated procedure and a manual one for feature location is presented by Perez et al [74].…”