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Register a new userInteractive Duplicate Search in Software Documentation
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Added by
Dmitrij Koznov Mr
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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Various software features such as classes, methods, requirements, and tests often have similar functionality. This can lead to emergence of duplicates in their descriptive documentation. Uncontrolled duplicates created via copy/paste hinder the process of documentation maintenance. Therefore, the task of duplicate detection in software documentation is of importance. Solving it makes planned reuse possible, as well as creating and using templates for unification and automatic generation of documentation. In this paper, we present an interactive process for duplicate detection that involves the user in order to conduct meaningful search. It includes a new formal definition of a near duplicate, a pattern-based, and the proof of its completeness. Moreover, we demonstrate the results of experimenting on a collection of documents of several industrial projects.
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Automatic documentation generation tools, or auto docs, are widely used to visualize information about APIs. However, each auto doc tool comes with its own unique representation of API information. In this paper, I use an information visualization analysis of auto docs to generate potential design principles for improving their usability. Developers use auto docs as a reference by looking up relevant API primitives given partial information, or leads, about its name, type, or behavior. I discuss how auto docs can better support searching and scanning on these leads, e.g. by providing more information-dense visualizations of method signatures.
Web search plays an integral role in software engineering (SE) to help with various tasks such as finding documentation, debugging, installation, etc. In this work, we present the first large-scale analysis of web search behavior for SE tasks using the search query logs from Bing, a commercial web search engine. First, we use distant supervision techniques to build a machine learning classifier to extract the SE search queries with an F1 score of 93%. We then perform an analysis on one million search sessions to understand how software engineering related queries and sessions differ from other queries and sessions. Subsequently, we propose a taxonomy of intents to identify the various contexts in which web search is used in software engineering. Lastly, we analyze millions of SE queries to understand the distribution, search metrics and trends across these SE search intents. Our analysis shows that SE related queries form a significant portion of the overall web search traffic. Additionally, we found that there are six major intent categories for which web search is used in software engineering. The techniques and insights can not only help improve existing tools but can also inspire the development of new tools that aid in finding information for SE related tasks.
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Software and IT industry in Pakistan have seen a dramatic growth and success in past few years and is expected to get doubled by 2020, according to a research. Software development life cycle comprises of multiple phases, activities and techniques that can lead to successful projects, and software evaluation is one of the vital and important parts of that. Software estimation can alone be the reason of product success factor or the products failure factor. To estimate the right cost, effort and resources is an art. But it is also very important to include the risks that may arise in the in a software project which can affect your estimates. In this paper, we highlight how the risks in Pakistan Software Industry can affect the estimates and how to mitigate them.
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