Enhancing Stakeholder Profiles to Improve Recommendations in Online Requirements Elicitation

Castro-Herrera, Carlos; Cleland‐Huang, Jane; Mobasher, Bamshad

doi:10.1109/re.2009.20

Cited by 42 publications

(43 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…K-Nearest Neighbor (kNN) learning strategy perform well in forum recommendations [38,41]. Two wellknown methods are also used for recommendation against the Standard KNN i.e.…”

Section: A Feature Recommendations Usingmentioning

confidence: 99%

A Survey on Feature Recommendation Techniques

Nazema¹

2015

IJRITCC

View full text Add to dashboard Cite

Abstract-Recommendation systems are a very common now days and it is used in a variety of applications. A recommender system that is designed to reduce the human effort of performing domain analysis. Domain analysis is the task in which we can find the commonality and difference between the different software"s of same domain "feature recommendation is very useful now a days. This approach relies on data mining techniques to discover common features across products as well as the relationship among these common features.In this paper we used different techniques which are used for domain analysis and feature recommendation. This approach mines descriptions of product from publicly available online product descriptions, uses a text mining and a novel incremental diffusive clustering algorithm to discover features in specific domain , uses association rule mining to know latent relationships between the features within the products of same domain and uses KNN algorithm which generates a probabilistic feature model that represents commonalities, variant.

show abstract

“…K-Nearest Neighbor (kNN) learning strategy perform well in forum recommendations [38,41]. Two wellknown methods are also used for recommendation against the Standard KNN i.e.…”

Section: A Feature Recommendations Usingmentioning

confidence: 99%

A Survey on Feature Recommendation Techniques

Nazema¹

2015

IJRITCC

View full text Add to dashboard Cite

show abstract

“…As an example, consider the recommendation system OPCI (Organizer and Promoter of Collaborative Ideas) [5,[7][8][9][10]. OPCI provides support for recommending stakeholders and topics and optional support for managing the actual discussion threads.…”

Section: Recommending Topicsmentioning

confidence: 99%

“…In addition to using a binary profile, major improvements can also be achieved [7] by augmenting the user profiles with additional known attributes about the users. This metadata can be incorporated into the ratings matrix, such that R D .r u;i / jU j .jAjCjI j/ , where the first A columns indicate that a user has an interest in a known attribute a of the domain.…”

Section: Profile Augmentation With Requirements Metadatamentioning

confidence: 99%

Recommendation Systems in Requirements Discovery

Hariri

Castro-Herrera

Cleland‐Huang

et al. 2013

Recommendation Systems in Software Engineering

Self Cite

View full text Add to dashboard Cite

Recommendation systems offer the opportunity for supporting and enhancing a wide variety of activities in requirements engineering. We discuss several potential uses. In particular we highlight the role of recommendation systems in online forums that are used for capturing and discussing feature requests. The recommendation system is used to mitigate problems introduced when face-toface communication is replaced with potentially high-volume online discussions. In this context, recommendation systems can be used to suggest relevant topics to stakeholders and conversely to recommend expert stakeholders for each discussion topic. We also explore the use of recommendation systems in the domain analysis process, where they can be used to recommend sets of features to include in new products.

show abstract

“…This description is parsed and then elements of the description are matched to features known by the recommender system (6). If matching features are found, they are presented to the user who is asked to confirm or reject their relevance (7). The feature recommender then generates additional feature recommendations and these are also presented to the user for feedback.…”

Section: Overviewmentioning

confidence: 99%

“…This is accomplished using the k-Nearest Neighbor (kNN) algorithm. This method has been shown to be efficient for recommending features and requirements [7]. For the purpose of feature recommendation, the similarity of the new product and each of the existing products in the Product × Feature matrix, M, is computed and the top k (20) most similar products are selected as neighbors of the new product.…”

Section: A Recommending Additional Featuresmentioning

confidence: 99%

Recommending source code for use in rapid software prototypes

McMillan¹,

Hariri²,

Poshyvanyk³

et al. 2012

2012 34th International Conference on Software Engineering (ICSE)

Self Cite

View full text Add to dashboard Cite

Abstract-Rapid prototypes are often developed early in the software development process in order to help project stakeholders explore ideas for possible features, and to discover, analyze, and specify requirements for the project. As prototypes are typically thrown-away following the initial analysis phase, it is imperative for them to be created quickly with little cost and effort. Tool support for finding and reusing components from open-source repositories offers a major opportunity to reduce this manual effort. In this paper, we present a system for rapid prototyping that facilitates software reuse by mining feature descriptions and source code from open-source repositories. Our system identifies and recommends features and associated source code modules that are relevant to the software product under development. The modules are selected such that they implement as many of the desired features as possible while exhibiting the lowest possible levels of external coupling. We conducted a user study to evaluate our approach and the results indicated that our proposed system returned packages that implemented more features and were considered more relevant than the state-of-the-art approach.

show abstract

Enhancing Stakeholder Profiles to Improve Recommendations in Online Requirements Elicitation

Cited by 42 publications

References 13 publications

A Survey on Feature Recommendation Techniques

A Survey on Feature Recommendation Techniques

Recommendation Systems in Requirements Discovery

Recommending source code for use in rapid software prototypes

Contact Info

Product

Resources

About