Integrating Natural Language Processing and Software Engineering

Yalla, Prasanth; Sharma, Nakul

doi:10.14257/ijseia.2015.9.11.12

Cited by 22 publications

(15 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are textual artefacts in analysis and design phases like Requirement Document and Software Design Specification. Quality attributes like performance, feature, reliability, aesthetics, and perception are need to be assessed in NLP software [14]. Most of the requirements are written in natural language text and this causes many issues like ambiguity, specification issues, and incompleteness.…”

Section: B Requirements Engineeringmentioning

confidence: 99%

From Requirements Engineering to UML using Natural Language Processing – Survey Study

Dawood

Sahraoui

2017

EJENG

View full text Add to dashboard Cite

In the paper process of moving from software requirements to UML diagrams has been studied. It shows the importance of this process and discusses many comparative studies in the field. A questionnaire related to the study was distributed worldwide to many research groups, academia, and industry to know the current status of using requirement management tools, knowledge of using UML in software development, frequently used UML diagrams, and the methodology used to generate UML diagrams from requirements. The paper emphasises that there is a need to do some important research in the area of requirements NLP to obtain UML diagrams, and generalize process of using automatic or semi-automatic methodology to generate UML diagrams from requirements.

show abstract

Section: B Requirements Engineeringmentioning

confidence: 99%

From Requirements Engineering to UML using Natural Language Processing – Survey Study

Dawood

Sahraoui

2017

EJENG

View full text Add to dashboard Cite

show abstract

“…During the Software Development process, NLP can be a useful tool provided that all the artifacts produced are plain text [6,7]. More in detail, Software Engineering exploits NLP to conduct a variety of activities that range from the detection of developers' emotion [8] and opinions of a software product [8] to the improvement of test case selection [9], code review [10], requirements engineering [11] and the detection of error-prone software components [12] Over time, NLP has been applied on code changes and their consequences in terms of code review and mapping of bug reports to relevant files [3,13].…”

Section: Introductionmentioning

confidence: 99%

Natural Language Processing Application on Commit Messages: A Case Study on HEP Software

Yang¹,

Ronchieri²,

Canaparo³

2022

Preprint

View full text Add to dashboard Cite

Version Control and Source Code Management Systems, such as GitHub, contain large amount ofunstructured historical information of software projects. Recent studies have introduced Natural Language Processing (NLP) to help software engineers retrieve information from very large collection of unstructured data. In this study, we have extended our previous study by increasing our datasets and ML and clustering techniques. Method: We have followed a complex methodology made up of various steps. Starting from the raw commit messages we have employed NLP techniques to build a structured database. We have extracted their main features and used as input of different clustering algorithms. Once labelled each entry, we have applied supervised machine learning techniques to build a prediction and classification model. Results: We have developed a machine learning-based model to automatically classify commit messages of a software project. Our model exploits a ground-truth dataset which includes commit messages obtained from various GitHub projects belonging to the HEP context. Conclusions: The contribution of this paper is two-fold: it proposes a ground-truth database; it provides a machine learning prediction model. They automatically identify the more change-proneness areas of code. Our model has obtained a very high average precision, recall and F1-score.

show abstract

“…NLP can be applied to every phase of the Software Development Life Cycle (SDLC) that deals with software development through the usage of tools, methods, processes and techniques [3]. NLP has been proved to be useful when the artifacts of SDLC phase or activity are plain text [4]. NLP has been applied to Software Engineering (SE) tasks in order conduct different activities, such as the detection of developers' emotions [5], the detection of the opinions of a software product [6], the improvement of test case selection [7], code review [8], requirements engineering [9] and the detection of error-prone software components [10].…”

Section: Introductionmentioning

confidence: 99%

Using Natural Language Processing to Extract Information from Unstructured code-change version control data: lessons learned

Ronchieri¹,

Canaparo²,

Yang³

2021

Proceedings of International Symposium on Grids &Amp; Clouds 2021 — PoS(ISGC2021)

View full text Add to dashboard Cite

Context: Natural Language Processing (NLP) is a branch of artificial intelligence that extracts information from language. In the field of software engineering, NLP has been employed to extract key information from free-form text, to generate models from the analysis of text or to categorize code changes according to their commit messages. In literature, most of the approaches NLP-based focused on the impact of code changes on program execution or software architecture. Objective: In this study, we have applied NLP to code-change data to identify patterns of software code modifications and used Machine Learning techniques to build a model that determines how software has evolved over time and identifies area of code that presents problems. Method: Considering that software projects use version control systems, such as github, to manage their code, we have collected software information by using git commands. These data contain different unstructured information about the various files in a project. Each modification entry includes a message that explains the reasons for the change. According to the content of the message, it is possible to identify key terms that can be used during the classification of the entries. Results: In this study, we have considered the change history of software available on github to the High Energy Physics community. With the use of NLP techniques we have cleaned the messages and extracted some key terms to categorize both software problems and some other changes performed by developers, like the addition of a third party dependency or a script that starts a given service. We have built a code change dictionary combining the terms already in existing literature with the ones gathered directly from the software and its github repository. Finally, we have applied some Machine Learning (ML) techniques to determine any connection between code changes and software problems: we have removed redundant entries to avoid any bias in the outcomes of the ML techniques. Conclusion: We show in detail our approach adopted to construct historical code change datasets of categorized commit messages by following a multi-label classification methodology. Our model performance seems promising in terms of accuracy, precision, recall and F1-score.

show abstract

Integrating Natural Language Processing and Software Engineering

Cited by 22 publications

References 23 publications

From Requirements Engineering to UML using Natural Language Processing – Survey Study

From Requirements Engineering to UML using Natural Language Processing – Survey Study

Natural Language Processing Application on Commit Messages: A Case Study on HEP Software

Using Natural Language Processing to Extract Information from Unstructured code-change version control data: lessons learned

Contact Info

Product

Resources

About