About NLBSE
Natural language processing (NLP) refers to automatic computational processing of human language, including both algorithms that take human-produced text as input and algorithms that produce natural-looking text as outputs. There is a widespread and growing usage of NLP approaches to optimize many aspects of the development process of software systems. Indeed, during the software development lifecycle, natural language artifacts are used and reused. The availability of natural language-based approaches and tools enabled the envisioning of methods for improving efficiency in software engineers, processes, and products.

This workshop aims to bring together researchers and industrial practitioners from NLP and the software engineering communities to collaborate, share experiences, provide directions for future research, and encourage the use of NLP techniques and tools for addressing software engineering-specific challenges.

Call for Papers
Researchers and practitioners are invited to submit:

Full papers (maximum of 8 pages, including references). Original research in NLP for SE, either empirical, theoretical, or showing the practical experience of using NLP techniques and/or NLP tools for addressing software engineering-specific challenges
Short and demonstration papers (maximum of 4 pages, including references). Work that describes novel techniques, tools, ideas, and positions that have yet to be fully developed; or are a discussion of the importance of a recently published NLP result by another author in setting a direction for the SE community, and/or the potential applicability (or not) of the result in an industrial context.
Position papers (maximum of 2 pages, including references). Contributions that analyze trends in NLBSE and raise issues of importance. Position papers are intended to seed discussion and debate at the workshop, and thus will be reviewed with respect to relevance and their ability to spark discussions.
Tool Competition entries (maximum of 4 pages, including references). We invite researchers, students, and tool developers to design innovative solutions to tackle the automated classification of (i) issue reports and (ii) code comments. For submissions of this kind, please refer to the instructions detailed in the tool competition webpage.
In all cases, papers should address a problem in the software engineering domain or combine elements of NLP research with other concerns in the software engineering lifecycle. Examples of problems in the software engineering domain include (but are not limited to): key information identification and extraction from natural language software artifacts; elicitation, modeling, and verification of requirements; generation of source code documentation; software verification and validation support; classification, summarization, and prioritization of development tasks; changes, developers, and solutions recommendation; maintenance effort minimization; quality assessment of natural language software artifacts.

The solution should apply NLP-based approaches and/or models such as (but not limited to) textual analysis, text summarization, topics or aspects modeling and extraction, machine translation, natural language parsing, semantic parsing, natural language generation, sentiment analysis, discourse analysis.