Req2Lib: A Semantic Neural Model for Software Library Recommendation

• URL: http://arxiv.org/abs/2005.11757v1
• Date: Sun, 24 May 2020 14:37:07 GMT
• Title: Req2Lib: A Semantic Neural Model for Software Library Recommendation
• Authors: Zhensu Sun, Yan Liu, Ziming Cheng, Chen Yang, Pengyu Che
• Abstract summary: We propose a novel neural approach called Req2Lib which recommends libraries given descriptions of the project requirement. We use a Sequence-to-Sequence model to learn the library linked-usage information and semantic information of requirement descriptions in natural language. Our preliminary evaluation demonstrates that Req2Lib can recommend libraries accurately.
• Score: 8.713783358744166
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Third-party libraries are crucial to the development of software projects. To get suitable libraries, developers need to search through millions of libraries by filtering, evaluating, and comparing. The vast number of libraries places a barrier for programmers to locate appropriate ones. To help developers, researchers have proposed automated approaches to recommend libraries based on library usage pattern. However, these prior studies can not sufficiently match user requirements and suffer from cold-start problem. In this work, we would like to make recommendations based on requirement descriptions to avoid these problems. To this end, we propose a novel neural approach called Req2Lib which recommends libraries given descriptions of the project requirement. We use a Sequence-to-Sequence model to learn the library linked-usage information and semantic information of requirement descriptions in natural language. Besides, we apply a domain-specific pre-trained word2vec model for word embedding, which is trained over textual corpus from Stack Overflow posts. In the experiment, we train and evaluate the model with data from 5,625 java projects. Our preliminary evaluation demonstrates that Req2Lib can recommend libraries accurately.

Related papers

• TrueLearn: A Python Library for Personalised Informational Recommendations with (Implicit) Feedback [4.575111313202425]
  This work describes the TrueLearn Python library, which contains a family of online learning Bayesian models. For the sake of interpretability and putting the user in control, the TrueLearn library also contains different representations to help end-users visualise the learner models.
  arXiv  Detail & Related papers  (2023-09-20T07:21:50Z)
• Private-Library-Oriented Code Generation with Large Language Models [52.73999698194344]
  This paper focuses on utilizing large language models (LLMs) for code generation in private libraries. We propose a novel framework that emulates the process of programmers writing private code. We create four private library benchmarks, including TorchDataEval, TorchDataComplexEval, MonkeyEval, and BeatNumEval.
  arXiv  Detail & Related papers  (2023-07-28T07:43:13Z)
• AndroLibZoo: A Reliable Dataset of Libraries Based on Software Dependency Analysis [6.342380566583581]
  We propose an automated approach to produce an accurate and up-to-date set of third-party libraries in the form of a dataset called AndroLibZoo. Our dataset, which we make available to the community, contains to date 34 813 libraries and is meant to evolve.
  arXiv  Detail & Related papers  (2023-07-24T08:36:38Z)
• torchgfn: A PyTorch GFlowNet library [56.071033896777784]
  torchgfn is a PyTorch library that aims to address this need. It provides users with a simple API for environments and useful abstractions for samplers and losses.
  arXiv  Detail & Related papers  (2023-05-24T00:20:59Z)
• SequeL: A Continual Learning Library in PyTorch and JAX [50.33956216274694]
  SequeL is a library for Continual Learning that supports both PyTorch and JAX frameworks. It provides a unified interface for a wide range of Continual Learning algorithms, including regularization-based approaches, replay-based approaches, and hybrid approaches. We release SequeL as an open-source library, enabling researchers and developers to easily experiment and extend the library for their own purposes.
  arXiv  Detail & Related papers  (2023-04-21T10:00:22Z)
• An Empirical Study of Library Usage and Dependency in Deep Learning Frameworks [12.624032509149869]
  pytorch, Caffe, and Scikit-learn are the most frequent combination in 18% and 14% of the projects. The developer uses two or three dl libraries in the same projects and tends to use different multiple dl libraries in both the same function and the same files.
  arXiv  Detail & Related papers  (2022-11-28T19:31:56Z)
• Code Librarian: A Software Package Recommendation System [65.05559087332347]
  We present a recommendation engine called Librarian for open source libraries. A candidate library package is recommended for a given context if: 1) it has been frequently used with the imported libraries in the program; 2) it has similar functionality to the imported libraries in the program; 3) it has similar functionality to the developer's implementation, and 4) it can be used efficiently in the context of the provided code.
  arXiv  Detail & Related papers  (2022-10-11T12:30:05Z)
• Lib-SibGMU -- A University Library Circulation Dataset for Recommender Systems Developmen [58.720142291102135]
  We opensource Lib-SibGMU - a university library circulation dataset. For a recommender architecture that consists of a vectorizer that turns the history of the books borrowed into a vector, we show that using the fastText model as a vectorizer delivers competitive results.
  arXiv  Detail & Related papers  (2022-08-25T22:10:18Z)
• SacreROUGE: An Open-Source Library for Using and Developing Summarization Evaluation Metrics [74.28810048824519]
  SacreROUGE is an open-source library for using and developing summarization evaluation metrics. The library provides Python wrappers around the official implementations of existing evaluation metrics. It provides functionality to evaluate how well any metric implemented in the library correlates to human-annotated judgments.
  arXiv  Detail & Related papers  (2020-07-10T13:26:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.