BMR: Benchmarking Metrics Recommender for Personnel issues in Software Development Projects

Abstract

This paper presents an architecture which applies document similarity measures to the documentation produced during the phases of software development in order to generate recommendations of process and people metrics for similar projects. The application makes a judgment of similarity of the Service Provision Offer (SPO) document of a new proposed project to a collection of Project History Documents (PHD), stored in a repository of unstructured texts. The process is carried out in three stages: firstly, clustering of the Offer document with the set of PHDs which are most similar to it; this provides the initial indication of whether similar previous projects exist, and signifies similarity. Secondly, determination of which PHD in the set is most comparable with the Offer document, based on various parameters: project effort, project duration (time), project resources (members/size of team), costs, and sector(s) involved, indicating comparability of projects. The comparable parameters are extracted using the GATE Natural Language Processing architecture. Lastly, a recommendation of metrics for the new project is made, which is based on the transferability of the metrics of the most similar and comparable PHD extracted, here referred to as recommendation.

Using data mining on student behavior and cognitive style data for improving e-learning systems: a case study

“…This problem is interesting since there are many stakeholders interested in recognizing students with excellent performance. 45,46,47,48 For the input data for the prediction, the model would use the data describing student behavior on e-learning resources (e.g. forums, discussions, quizzes, posts, assignments) as described in the previous subsection.…”

ReSySTER: A hybrid recommender system for Scrum team roles based on fuzzy and rough sets

“…Focusing on the technologies employed in ReSySTER, namely, rough sets, fuzzy techniques and semantic technologies, these efforts are also present in the software engineering staffing literature: rough sets (Imai et al, 2008)), fuzzy techniques (Strnad and Guid, 2010;Wang and Lin, 2003) and semantic technologies (Garcia-Crespo et al, 2009;Valencia-Garcia et al, 2010). The combination of methods for improving the performance of intelligent systems is a common issue addressed by multiple authors in multiple domains (Li and Chiang, 2011;Nowicki, 2010;Mahmoud, 2011;Zhong and Skowron, 2001).…”

Towards an ontology modeling tool. A validation in software engineering scenarios

“…Software Engineering is one of them. As a result of this there are many initiatives reported in the literature that employ semantic technologies in aspects like requirements (Chicaiza, Ló pez, Piedra, Marti´nez, & Tovar, 2010), analysis (Tappolet, Kiefer, & Bernstein, 2010, modeling (Gallardo, Molina, Bravo, Redondo, & Collazos, 2011Martinho, Varajao, & Domingos, 2010;Sicilia, Sicilia, Sánchez Alonso, García Barrioca nal, & Pontikaki, 2009), teaming (Soto Acosta, Casado Lumbreras, & Cabezas Isla, 2010Valencia García, García Sánchez, Castellanos Nieves, Fernández Breis, & Toval, 2010), cooperative building (Tacla, Freddo, Paraiso, Ramos, & Sato, 2011), software metrics (García Crespo, Colomo Palacios, Gómez Berbís, & Mencke, 2009), reuse (Shiva & Shala, 2008) or quality management to cite some of the most relevant and recent cases.…”