Finding Source Code On The Web For Remix And Reuse
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Finding Source Code on the Web for Remix and Reuse
Author: Susan Elliott Sim
language: en
Publisher: Springer Science & Business Media
Release Date: 2013-06-04
In recent years, searching for source code on the web has become increasingly common among professional software developers and is emerging as an area of academic research. This volume surveys past research and presents the state of the art in the area of "code retrieval on the web." This work is concerned with the algorithms, systems, and tools to allow programmers to search for source code on the web and the empirical studies of these inventions and practices. It is a label that we apply to a set of related research from software engineering, information retrieval, human-computer interaction, management, as well as commercial products. The division of code retrieval on the web into snippet remixing and component reuse is driven both by empirical data, and analysis of existing search engines and tools. Contributors include leading researchers from human-computer interaction, software engineering, programming languages, and management. "Finding Source Code on the Web for Remix and Reuse" consists of five parts. Part I is titled "Programmers and Practices," and consists of a retrospective chapter and two empirical studies on how programmers search the web for source code. Part II is titled "From Data Structures to Infrastructures," and covers the creation of ground-breaking search engines for code retrieval required ingenuity in the adaptation of existing technology and in the creation of new algorithms and data structures. Part III focuses on "Reuse: Components and Projects," which are reused with minimal modification. Part IV is on "Remix: Snippets and Answers," which examines how source code from the web can also be used as solutions to problems and answers to questions. The book concludes with Part V, "Looking Ahead," that looks at future programming and the legalities of software reuse and remix and the implications of current intellectual property law on the future of software development. The story, "Richie Boss: Private Investigator Manager," was selected as the winner of a crowdfunded short story contest."
Mining Software Engineering Data for Software Reuse
Author: Themistoklis Diamantopoulos
language: en
Publisher: Springer Nature
Release Date: 2020-03-30
This monograph discusses software reuse and how it can be applied at different stages of the software development process, on different types of data and at different levels of granularity. Several challenging hypotheses are analyzed and confronted using novel data-driven methodologies, in order to solve problems in requirements elicitation and specification extraction, software design and implementation, as well as software quality assurance. The book is accompanied by a number of tools, libraries and working prototypes in order to practically illustrate how the phases of the software engineering life cycle can benefit from unlocking the potential of data. Software engineering researchers, experts, and practitioners can benefit from the various methodologies presented and can better understand how knowledge extracted from software data residing in various repositories can be combined and used to enable effective decision making and save considerable time and effort through software reuse. Mining Software Engineering Data for Software Reuse can also prove handy for graduate-level students in software engineering.
Recommendation Systems in Software Engineering
Author: Martin P. Robillard
language: en
Publisher: Springer Science & Business
Release Date: 2014-04-30
With the growth of public and private data stores and the emergence of off-the-shelf data-mining technology, recommendation systems have emerged that 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 evaluation. The book is divided into three parts: “Part I – Techniques” introduces basics for building recommenders in software engineering, including techniques for collecting and processing software engineering data, but also for presenting recommendations to users as part of their workflow. “Part II – Evaluation” summarizes methods and experimental designs for evaluating recommendations in software engineering. “Part III – Applications” describes needs, issues and solution concepts involved in entire recommendation systems for specific software engineering tasks, focusing on the engineering insights required to make effective recommendations. The book is complemented by the webpage rsse.org/book, which includes free supplemental materials for readers of this book and anyone interested in recommendation systems in software engineering, including lecture slides, data sets, source code, and an overview of people, groups, papers and tools with regard to recommendation systems in software engineering. The book is particularly well-suited for graduate students and researchers building new recommendation systems for software engineering applications or in other high-tech fields. It may also serve as the basis for graduate courses on recommendation systems, applied data mining or software engineering. Software engineering practitioners developing recommendation systems or similar applications with predictive functionality will also benefit from the broad spectrum of topics covered.