2,512 research outputs found
Experts Recommender System Using Technical and Social Heuristics
Nowadays, successful cooperation and collaboration among developers is crucial to build
successful projects in distributed software system development (DSSD). Assigning wrong
developers to a specific task not only affects the performance of a component of this task but
also affects other components since these projects are composed of dependent components.
Another aspect that should be considered when teams are built is the social relationships between
the members; disagreements between these members also affect the project team’s performance.
These two aspects might cause a project’s failure or delay. Therefore, they are important to
consider when teams are created. In this thesis, we developed an Expert Recommender System
Framework (ERSF) that assists developers (Active Developers) to find experts who can help
them complete or fix the bugs in the code at hand. The ERSF analyzes the developer technical
expertise on similar code fragments to the one they need help on assuming that those who have
worked on similar fragments might understand and help the Active Developer; also, it analyzes
their social relationships with the Active Developer as well as their social activities within the
DSSD. Our work is also concerned with improving the system performance and
recommendations by tracking the developer communications through our ERSF in order to keep
developer profiles up-to-date. Technical expertise and sociality are measured using a
combination of technical and social heuristics. The recommender system was tested using
scenarios derived from real software development data, and its recommendations compared
favourably to recommendations that humans were asked to make in the same scenarios; also,
they were compared to the recommendations of the NaiveBayes and other machine learning
algorithms. Our experiment results show that ERSF can recommend experts with good to
excellent accuracy
Recommender systems in model-driven engineering: A systematic mapping review
Recommender systems are information filtering systems used in many online applications like music and video broadcasting and e-commerce platforms. They are also increasingly being applied to facilitate software engineering activities. Following this trend, we are witnessing a growing research interest on recommendation approaches that assist with modelling tasks and model-based development processes. In this paper, we report on a systematic mapping review (based on the analysis of 66 papers) that classifies the existing research work on recommender systems for model-driven engineering (MDE). This study aims to serve as a guide for tool builders and researchers in understanding the MDE tasks that might be subject to recommendations, the applicable recommendation techniques and evaluation methods, and the open challenges and opportunities in this field of researchThis work has been funded by the European Union’s Horizon 2020
research and innovation programme under the Marie Skłodowska-Curie
Grant Agreement No. 813884 (Lowcomote [134]), by the Spanish
Ministry of Science (projects MASSIVE, RTI2018-095255-B-I00, and
FIT, PID2019-108965GB-I00) and by the R&D programme of Madrid
(Project FORTE, P2018/TCS-431
Combining configuration and recommendation to enable an interactive guidance of product line configuration
This paper is interested in e-commerce for complex configurable products/systems. E-commerce makes a wide use of recommendation techniques to help customers identify relevant products or services in large collections of offers. One particular way to achieve this is to offer customers a panel of options among which they can select their preferred ones. A trend in the industry is to go a step further, beyond the selection of pre-defined products from a catalogue by handling products customization. The systems engineering community has shown that, based on product line engineering methods, techniques and tools, it is possible to produce customized products efficiently and at low cost. The problem is that there are usually so many products in a PL that it is impossible to specify all of them explicitly, and therefore traditional recommendation techniques cannot be simply applied. This paper proposes an approach that combines two complementary forms of guidance: configuration and recommendation, to help customers define their own products out of a product line specification. The proposed approach, called interactive configuration supports the combination by organizing the configuration process in a series of partial configurations where decisions are made by the recommendation. This paper illustrates this process by applying it to an example with the content based method for recommendation and the a priori configuration approach
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