Titlebook: Recommendation Systems in Software Engineering; Martin P. Robillard,Walid Maalej,Thomas Zimmermann Book 2014 Springer-Verlag Berlin Heidel

厌烦

978-3-662-52404-6Springer-Verlag Berlin Heidelberg 2014

contrast-medium

Recommendation Systems in-the-Smallby a RITS through the use of heuristics. Several examples drawn from the literature illustrate the applications and designs of RITSs. We provide an introduction to the development of the heuristics typically needed by a RITS. We discuss the general limitations of RITSs.

conscribe

Mining Bug Dataers, success, and finally profit. This chapter serves as a hand-on tutorial on how to mine bug reports, relate them to source code, and use the knowledge of bug fix locations to model, estimate, or even predict source code quality. This chapter also discusses risks that should be addressed before one can achieve reliable recommendation systems.

卜闻

带来墨水

露天历史剧

尾巴

Book 2014 specifically address the unique challenges of navigating and interpreting software engineering data..This book collects, structures and formalizes knowledge on recommendation systems in software engineering. It adopts a pragmatic approach with an explicit focus on system design, implementation, and

不能妥协

An Introduction to Recommendation Systems in Software Engineering,g include the source code and change history of the software, discussion lists and forums, issue databases, component technologies and their learning resources, and the development environment. The technical nature, size, and dynamicity of these information spaces motivate the development of a speci

Cholecystokinin

Basic Approaches in Recommendation Systemsent-based filtering, and knowledge-based recommendation. We first discuss principles of the underlying algorithms based on a running example. Thereafter, we provide an overview of hybrid recommendation approaches which combine basic variants. We conclude this chapter with a discussion of newer algor

Isometric

Recommendation Systems in-the-Smallr the infrastructure needed to support and to maintain an RSSE; moreover, it can be computationally expensive. This chapter examines recommendation systems in-the-small (RITSs), which do not rely on data mining. Instead, they take small amounts of data from the developer’s local context as input and