Clustering Glossary Terms Extracted from Large-Sized Software Requirements using FastText

Bhatia, Kushagra; Mishra, Siba; Sharma, Arpit

doi:10.1145/3385032.3385039

Cited by 12 publications

(4 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…• Glossaries [127], [128], which define technical terms which are specific to an application domain. • Goal Models [129], that focus on the objectives which a system should achieve through the cooperation of actors in the intended software and the environment.…”

Section: ) Requirements Extractionmentioning

confidence: 99%

“…Aggregation-based [11], [47], [56], [58], [59], [64], [66], [76], [84], [91], [98], [101], [104], [128] RNN-based [43], [51], [54], [67], [112], [137] CNN-based [39], [50] TABLE 3. the used statement embedding techniques with their related paper…”

Section: Related Papersmentioning

confidence: 99%

“…--Using the average of words embedding to represent statements [66], [101], [128]. --For BERT-based word embedding, a special token ([CLS]) is added to the beginning of the sentence and is used to calculate an aggregated representation of the statement [11], [64], [84], [91], [98].…”

Section: Related Papersmentioning

confidence: 99%

See 2 more Smart Citations

The Use of NLP-Based Text Representation Techniques to Support Requirement Engineering Tasks: A Systematic Mapping Review

2022

View full text Add to dashboard Cite

Natural Language Processing (NLP) is widely used to support the automation of different Requirements Engineering (RE) tasks. Most of the proposed approaches start with various NLP steps that analyze requirements statements, extract their linguistic information, and convert them to easy-toprocess representations, such as lists of features or embedding-based vector representations. These NLPbased representations are usually used at a later stage as inputs for machine learning techniques or rulebased methods. Thus, requirements representations play a major role in determining the accuracy of different approaches. In this paper, we conducted a survey in the form of a systematic literature mapping (classification) to find out (1) what are the representations used in RE tasks literature, (2) what is the main focus of these works, (3) what are the main research directions in this domain, and ( 4) what are the gaps and potential future directions. After compiling an initial pool of 2,227 papers, and applying a set of inclusion/exclusion criteria, we obtained a final pool containing 104 relevant papers. Our survey shows that the research direction has changed from the use of lexical and syntactic features to the use of advanced embedding techniques, especially in the last two years. Using advanced embedding representations has proved its effectiveness in most RE tasks (such as requirement analysis, extracting requirements from reviews and forums, and semantic-level quality tasks). However, representations that are based on lexical and syntactic features are still more appropriate for other RE tasks (such as modeling and syntax-level quality tasks) since they provide the required information for the rules and regular expressions used when handling these tasks. In addition, we identify four gaps in the existing literature, why they matter, and how future research can begin to address them.

show abstract

Section: ) Requirements Extractionmentioning

confidence: 99%

Section: Related Papersmentioning

confidence: 99%

See 1 more Smart Citation

The Use of NLP-Based Text Representation Techniques to Support Requirement Engineering Tasks: A Systematic Mapping Review

2022

View full text Add to dashboard Cite

show abstract

“…In retrospect, while this topic may seem well-researched, it is far from being a thoroughly explored area among researchers. Only a few studies have concentrated on mining glossary terms related to requirements [39,126], or extracting glossary terms from source code summarization [389]. These approaches predominantly provide automated solutions for extracting keywords from various artifacts, with a focus on reverse engineering.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Software Quality Maze: Detecting Scattered and Tangled Crosscutting Quality Concerns in Source Code in Support of Maintenance Tasks

Krasniqi

View full text Add to dashboard Cite

Software quality attributes, such as reliability, security, and usability, are often well-defined and understood at the requirement level. They lay the ground foundation necessary to achieve high-quality, robust, user-friendly, and trustworthy software systems. However, when addressing these attributes at the code level, two significant challenges emerge. First, they tend to scatter across the codebase due to improper encapsulation of object-oriented classes, hampering the visibility of quality-related components across the codebase. Second, they become tangled within a single module due to intricate interdependencies with functional aspects of the code. Addressing quality concerns in the presence of scattered and tangled code can lead to unforeseen issues. For example, software developers may inadvertently introduce new and latent bugs or incorrectly implement code components deviating from the original system-wide requirements. To tackle these pressing issues, this dissertation proposes a series of state-of-the-art solutions integrating ML-based techniques and NLP-based techniques, including static program analysis techniques, to automatically and effectively detect and repair quality concerns present at the code level, even when scattered across the codebase. Additionally, we introduce program structural analysis and change impact analysis, complemented by other unsupervised ML-based techniques, to disentangle quality-related changes from functional ones, to gain a holistic understanding of a particular maintenance task. From a practical stance, our techniques can be integrated for quality risk assessment purposes and incorporated into safety-critical continuous integration systems to monitor the evolving nature of quality concerns. Furthermore, they can enhance bidirectional traceability across evolving software artifacts, such as bug reports, commit messages, and source code, and facilitate bug triaging systems for prioritizing the resolution of highly impacted quality-related bugs.

show abstract

Abbreviation-Expansion Pair Detection for Glossary Term Extraction

Hasso

Großer

Aymaz

et al. 2022

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Clustering Glossary Terms Extracted from Large-Sized Software Requirements using FastText

Cited by 12 publications

References 15 publications

The Use of NLP-Based Text Representation Techniques to Support Requirement Engineering Tasks: A Systematic Mapping Review

The Use of NLP-Based Text Representation Techniques to Support Requirement Engineering Tasks: A Systematic Mapping Review

Exploring the Software Quality Maze: Detecting Scattered and Tangled Crosscutting Quality Concerns in Source Code in Support of Maintenance Tasks

Abbreviation-Expansion Pair Detection for Glossary Term Extraction

Contact Info

Product

Resources

About