APR: Architectural Pattern Recommender
This paper proposes Architectural Pattern Recommender (APR) system which helps in such architecture selection process. Main contribution of this work is in replacing the manual effort required to identify and analyse relevant architectural patterns in context of a particular set of software requirements. Key input to APR is a set of architecturally significant use cases concerning the application being developed. Central idea of APR's design is two folds: a) transform the unstructured information about software architecture design into a structured form which is suitable for recognizing textual entailment between a requirement scenario and a potential architectural pattern. b) leverage the rich experiential knowledge embedded in discussions on professional developer support forums such as Stackoverflow to check the sentiment about a design decision. APR makes use of both the above elements to identify a suitable architectural pattern and assess its suitability for a given set of requirements. Efficacy of APR has been evaluated by comparing its recommendations for "ground truth" scenarios (comprising of applications whose architecture is well known).
Code (0)
등록된 구현이 없습니다.
Tasks
Natural Language InferenceSimilar Papers 제목 키워드 기반
Pre-trained LLMs Meet Sequential Recommenders: Efficient User-Centric Knowledge Distillation
Sequential recommender systems have achieved significant success in modeling temporal user behavior but remain limited in capturing rich user semantics beyond interaction patterns. Large Language Models (LLMs) present op…
Knowledge DistillationAdvanced Customer Activity Prediction based on Deep Hierarchic Encoder-Decoders
Product recommender systems and customer profiling techniques have always been a priority in online retail. Recent machine learning research advances and also wide availability of massive parallel numerical computing has…
Activity PredictionProduct RecommendationRecommendation SystemsTime Series AnalysisContext Features Are Cheap: Rank-Aware Decomposition for Efficient Feature Interaction in Recommender Systems
Modern industrial recommender systems use a deep ranking model to score N candidates against the same user and context features. Standard implementations broadcast context features early in the forward pass, redundantly …
Collaborative filtering, K-nearest neighbor and cosine similarity in home decor recommender systems
An architectural framework, based on collaborative filtering using K-nearest neighbor and cosine similarity, was developed and implemented to fit the requirements for the company DecorRaid. The aim of the paper is to tes…
Collaborative FilteringRecommendation SystemsHybrid Recommender System Based on Personal Behavior Mining
Recommender systems are mostly well known for their applications in e-commerce sites and are mostly static models. Classical personalized recommender algorithm includes item-based collaborative filtering method applied i…
Collaborative FilteringRecommendation SystemsSequential Pattern Mining