Applications of natural language processing in software traceability: A systematic mapping study

1 IntroductionOrganizations focus on automate their processes in order to improve efficiency, reduce costs, and/or reduce human beings' errors in an easy and rapid manner. Business process management (BPM) methods provide a solution to this issue. In this context, information systems like CRM, ERP, SCM, etc. have known an increasing demand. The main problem consists of the length of the business process specifications. If new regulations appear, these specifications must be adapted. Manually extraction of visual models is time consuming. During time, a series of solutions were proposed. The literature shows a crowd of approaches that extracts data models [1], [2], [3], and process models [4], [5] from Natural Language (NL) text. This paper aims to analyze the Natural Language Processing (NLP) techniques and tools used in order to provide different types of visual representations.On the last few years, approaches based on NLP have been developed in order to automate process conversion from NL text. NLP plays an important role in NL text analysis as NLP tries to understand speech and text as humans beings would do. Colloquialism, abbreviations or typos make this task a challenging one. NLP has the origins in 1950s when Alan Turing proposed the Turing test [6], by introducing the imitation game. Since then, the literature shows a plethora of NLP tools [7], [8], [9], [10] using several machine learning techniques, our focus being on those applied on data models and process models discovery from NL text, starting with the first language parser [11] to the actual ones like NLTk [7], [8], ANTLR1, etc.Linguistic analysis is closely tied to NLP. Liddy [12] highlights 7 levels of linguistic analysis: a) Phonetic or Phonological level: how words are pronounced, b) Morphological level: prefixes, suffixes and roots analysis, c) Lexical level: word level analysis including lexical meaning and Part-Of-Speech (POS) analysis, d) Syntactic level: grammatical analysis of words in a sentence, e) Semantic level: determining the possible meanings of sentences, f) Discourse level: interpreting structure and meaning for texts larger than a sentence, g) Pragmatic level: understanding the purpose of a language.Some of the problems approached by NLP are: POS tagging, parsing, Named Entity Recognition (NER), chunking, Semantic Role Labeling (SRL). Anaphora resolution [13] refers to the interpretation of the link between the anaphor and its antecedents.The remainder of the paper is organized as follows: Section 2 briefly outlines the NLP domain, describing the levels of linguistic analysis and the main NLP approaches of analyzing NL requirements. Section 3 focuses on data and process models extraction from NL text. This section makes an introduction to Object Oriented Analysis and Business Process Modeling and analyses the existing tools that discover data and process models from text. Subsequently, Section 4 summarizes the results of this work and the conclusions are drawn in Section 5.2NLPA detailed review on NLP is given in [14] and in [15]. Jones [14] divides the history of NLP into four phases: the first starts at the beginning of the 1940s and lasts to the late 1960s, the second begins from the end of 60s and lasts to the end of 70s, the third is represented by late 80s, where the fourth phase starts in the late of 90s. Next, we will detail each phase as they were defined in [14] and [15]. First phase treated machine translation issues, while the second focused on artificial intelligence. The third phase can be called grammatico-logical phase, which is followed by the lexical phase. A fifth phase is proposed in [16] where formal theories and statistical data are combined. Since this study was published first in 1994 and then it was re-organized in 2001 we can add the sixth phase: from 2000 until present where NLP techniques are combined in order to contribute to visual models extraction.Software requirements are usually written in NL which is asymmetric and irregular [17]. …

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Context:</i> User stories have been widely accepted as artifacts to capture the user requirements in agile software development. They are short pieces of texts in a semi-structured format that express requirements. Natural language processing (NLP) techniques offer a potential advantage in user story applications. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective:</i> Conduct a systematic literature review to capture the current state-of-the-art of NLP research on user stories. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Method:</i> The search strategy is used to obtain relevant papers from SCOPUS, ScienceDirect, IEEE Xplore, ACM Digital Library, SpringerLink, and Google Scholar. Inclusion and exclusion criteria are applied to filter the search results. We also use the forward and backward snowballing techniques to obtain more comprehensive results. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</i> The search results identified 718 papers published between January 2009 to December 2020. After applying the inclusion/exclusion criteria and the snowballing technique, we identified 38 primary studies that discuss NLP techniques in user stories. Most studies used NLP techniques to extract aspects of who, what, and why from user stories. The purpose of NLP studies in user stories is broad, ranging from discovering defects, generating software artifacts, identifying the key abstraction of user stories, and tracing links between model and user stories. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusion:</i> NLP can help system analysts manage user stories. Implementing NLP in user stories has many opportunities and challenges. Considering the exploration of NLP techniques and rigorous evaluation methods is required to obtain quality research. As with NLP research in general, the ability to understand a sentence’s context continues to be a challenge.

Applications in the World Wide Web aggregate vast amounts of information from different data sources. The aggregation process is often implemented with Extract, Transform and Load (ETL) processes. Usually ETL processes require information for aggregation available in structured formats, e. g. XML or JSON. In many cases the information is provided in natural language text which makes the application of ETL processes impractical. Due to the fact that information is provided in natural language, Information Extraction (IE) systems have been evolved. They make use of Natural Language Processing (NLP) tools to derive meaning from natural language text. State-of-the-art NLP tools apply Machine Learning methods. These NLP tools perform on newspapers with good quality, but they drop accuracy in other domains. However, to improve the quality for IE systems in specific domains often NLP tools are trained on domain specific text which is a time consuming process. This paper introduces an approach using a Continuous Integration pipeline for organizing and monitoring the annotation process on domain specific corpora.

The inspection of documents written in natural language with computers has become feasible thanks to the advances in Natural Language Processing (NLP) techniques. However, certain applications require a deeper semantic analysis of the text to produce good results. In this article, we present an exploratory study of semantic-aware NLP techniques for discovering latent concerns in use case specifications. For this purpose, we propose two NLP techniques, namely: semantic clustering and semantically-enriched rules. After evaluating these two techniques and comparing them with a technique developed by other researchers, results have showed that semantic NLP techniques hold great potential for detecting candidate concerns. Particularly, if these techniques are properly configured, they can help to reduce the efforts of requirement analysts and promote better quality in software development.

Historical data poses unique challenges to natural language processing (NLP) and information retrieval (IR) tools, including digitization errors, lack of annotated data, and diachronic-specific issues. However, the increasing recognition of the value in historical documents has promoted efforts to semantically enrich and optimize their analysis. This article contributes to this endeavour by enriching the Corpus de Textos Antigos through NLP tools and techniques to enhance its usability and support research. The corpus undergoes linguistic annotation, including part-of-speech tagging, lemma annotation and named entity recognition (NER). Subsequently, the article delves into the tasks of entity disambiguation and entity linking, which involve identifying and disambiguating named entities by referring to a knowledge base (KB). Addressing the challenges posed by factors such as text state, epoch and the chosen KB, the article presents insights into related work, annotation results and the linguistic interest of a medieval annotated corpus for named entities. It concludes by discussing the challenges and providing avenues for future research in this domain.

Aspirin-exacerbated respiratory disease (AERD) is an acquired inflammatory condition characterized by the presence of asthma, chronic rhinosinusitis with nasal polyposis, and respiratory hypersensitivity reactions on ingestion of aspirin or other nonsteroidal anti-inflammatory drugs (NSAIDs). Despite AERD having a classic constellation of symptoms, the diagnosis is often overlooked, with an average of greater than 10 years between the onset of symptoms and diagnosis of AERD. Without a diagnosis, individuals will lack opportunities to receive effective treatments, such as aspirin desensitization or biologic medications. Our aim was to develop a combined algorithm that integrates both natural language processing (NLP) and machine learning (ML) techniques to identify patients with AERD from an electronic health record (EHR). A rule-based decision tree algorithm incorporating NLP-based features was developed using clinical documents from the EHR at Mayo Clinic. From clinical notes, using NLP techniques, 7 features were extracted that included the following: AERD, asthma, NSAID allergy, nasal polyps, chronic sinusitis, elevated urine leukotriene E4 level, and documented no-NSAID allergy. MedTagger was used to extract these 7 features from the unstructured clinical text given a set of keywords and patterns based on the chart review of 2 allergy and immunology experts for AERD. The status of each extracted feature was quantified by assigning the frequency of its occurrence in clinical documents per subject. We optimized the decision tree classifier's hyperparameters cutoff threshold on the training set to determine the representative feature combination to discriminate AERD. We then evaluated the resulting model on the test set. The AERD algorithm, which combines NLP and ML techniques, achieved an area under the receiver operating characteristic curve score, sensitivity, and specificity of 0.86 (95% CI 0.78-0.94), 80.00 (95% CI 70.82-87.33), and 88.00 (95% CI 79.98-93.64) for the test set, respectively. We developed a promising AERD algorithm that needs further refinement to improve AERD diagnosis. Continued development of NLP and ML technologies has the potential to reduce diagnostic delays for AERD and improve the health of our patients.

This Chapter discusses the application of Natural Language Processing (NLP) in the legal domain for entity identification and the generation of hierarchy-based applications. NLP techniques, such as Named Entity Recognition (NER) and information extraction, are utilized to analyse legal documents, extract key information, and understand contextual relationships. The integration of NLP facilitates tasks such as legal document summarization, contract analysis, automated document generation, and legal research, thereby enhancing the efficiency and accuracy of legal processes. The creation of hierarchical structures within documents, language translation, sentiment analysis, and compliance monitoring further contribute to the comprehensive utilization of NLP in the legal field. The abstract highlights the transformative impact of NLP in streamlining legal workflows, improving information retrieval, and ensuring compliance with evolving legal standards. Most judicial systems in the world, centered around the Supreme Court as the highest-level court, grapples with a substantial caseload, receiving over 7,500 cases annually but hearing fewer than 150. The Supreme Court's unique role in interpreting the constitutionality of laws and establishing precedents underscores its significant influence. Challenges arise from the overwhelming caseload, leading to increased use of plea bargains and potential concerns about justice. To address these issues, a proposed research initiative suggests employing machine learning and natural language processing to accelerate Supreme Court decisions, aiming to uncover pivotal aspects that greatly influence the outcomes, and enhance our comprehension on India legal system's functioning and constraints. This chapter presents a methodology for sentiment-based summarization of legal documents using Natural Language Processing (NLP) techniques. The process involves pre-processing the text, conducting sentiment analysis to label sentences as positive, negative, or neutral, and then summarizing the document while incorporating the identified sentiments. Special attention is paid to legal-specific considerations such as terminology and context.

The role of natural language processing (NLP) techniques, such as parsing and semantic analysis, is described within current language tutoring systems. Significant trends are distinguished in the exploitation of these techniques, design issues and tradeoffs are examined, and current and potential contributions of NLP technology are discussed with respect to instructional theory and educational practice. Limitations and problems are addressed in using NLP tools for teaching, and approaches to assessment are considered.

INTRODUCTION: YouTube, the online multimedia platform, remains an untapped resource for surgical procedure content for education and research. Recent efforts to identify this content have lacked the diversity, volume and depth of the expanding corpus of surgical content on the site. We leverage computer vision (CV) and natural language processing (NLP) techniques to index and characterize the vast amount of open, non-minimally invasive surgical content on YouTube. METHODS: Twenty-three surgical procedure terms were used to programmatically query and collect videos, resulting in 9,197 results. Video-level metadata from YouTube were collected. Using a CV model and a NLP tool developed by our group, each video was analyzed for detailed scene understanding (e.g., action, tool, and hand detection) and medical and procedure tags from the Unified Medical Language System (UMLS) were generated. RESULTS: We identified 477, 270, and 556 videos related to head and neck, breast, and gastrointestinal surgery, respectively. 10.1 million frames were analyzed, and 11.9, 3.9, and 5.6 million, hands, surgical tools, and instances of surgical activity, respectively, were detected. 59,776 UMLS tags were also generated from video metadata. UMLS tags and temporal, “surgical signatures” were generated for each type and category of surgical procedures (Fig. 1).Figure 1CONCLUSION: Using novel CV and NLP techniques, we efficiently identified and characterized a large amount of open surgical videos in an automated way that exceeds previous efforts in scope, volume and depth. This technique enables the creation of a continuously expanding and comprehensive, surgical video repository to enhance surgical education and research.

SummaryContext: Software performance is crucial for ensuring the quality of software products. As one of the non‐functional requirements, the few efforts devoted to software performance have often been neglected until a later phase in the software development life cycle (SDLC). The lack of clarity of what software performance research literature is available prevents researchers from understanding what software performance research fields are available. It also creates difficulty for practitioners to adopt state‐of‐the‐art software performance techniques. Software performance research is not as organized as other established research topics such as software testing. Thus, it is essential to conduct a systematic mapping study as a first step to provide an overview of the latest research literature available in software performance. Objective: The objective of this systematic mapping study is to survey and map software performance research literature into suitable categories and to synthesize the literature data for future access and reference. Method: This systematic mapping study conducts a manual examination by querying research literature in noble journals and proceedings in software engineering in the past decade. We examine each paper manually and identify primary studies for further analysis and synthesis according to the pre‐defined inclusion criteria. Lastly, we map the primary studies based on their corresponding classification category. Results: This systematic mapping study provides a state‐of‐the‐art literature mapping in software performance research. We have carefully examined 222 primary studies out of 2000+ research literature. We have identified six software performance research categories and 15 subcategories. We generate the primary study mapping and report five research findings. Conclusions: Unlike established research fields, it is unclear what types of software performance research categories are available to the community. This work takes the systematic mapping study approach to survey and map the latest software performance research literature. The study results provide an overview of the paper distribution and a reference for researchers to navigate research literature on software performance.

In the past few years there has been a growing interest in the employment of verbal productions as digital biomarkers, namely objective, quantifiable behavioural data that can be collected and measured by means of digital devices, allowing for a low-cost pathology detection, classification and monitoring. Numerous research papers have been published on the automatic detection of subtle verbal alteration, starting from written texts, raw speech recordings and transcripts, and such linguistic analysis has been singled out as a cost-effective method for diagnosing dementia and other medical conditions common among elderly patients (e.g., cognitive dysfunctions associated with metabolic disorders, dysarthria). To provide a critical appraisal and synthesis of evidence concerning the application of natural language processing (NLP) techniques for clinical purposes in the geriatric population. In particular, we discuss the state of the art on studying language in healthy and pathological ageing, focusing on the latest research efforts to build non-intrusive language-based tools for the early identification of cognitive frailty due to dementia. We also discuss some challenges and open problems raised by this approach. We performed a scoping review to examine emerging evidence about this novel domain. Potentially relevant studies published up to November 2021 were identified from the databases of MEDLINE, Cochrane and Web of Science. We also browsed the proceedings of leading international conferences (e.g., ACL, COLING, Interspeech, LREC) from 2017 to 2021, and checked the reference lists of relevant studies and reviews. The paper provides an introductory, but complete, overview of the application of NLP techniques for studying language disruption due to dementia. We also suggest that this technique can be fruitfully applied to other medical conditions (e.g., cognitive dysfunctions associated with dysarthria, cerebrovascular disease and mood disorders). Despite several critical points need to be addressed by the scientific community, a growing body of empirical evidence shows that NLP techniques can represent a promising tool for studying language changes in pathological aging, with a high potential to lead a significant shift in clinical practice. What is already known on this subject Speech and languages abilities change due to non-pathological neurocognitive ageing and neurodegenerative processes. These subtle verbal modifications can be measured through NLP techniques and used as biomarkers for screening/diagnostic purposes in the geriatric population (i.e., digital linguistic biomarkers-DLBs). What this paper adds to existing knowledge The review shows that DLBs can represent a promising clinical tool, with a high potential to spark a major shift to dementia assessment in the elderly. Some challenges and open problems are also discussed. What are the potential or actual clinical implications of this work? This methodological review represents a starting point for clinicians approaching the DLB research field for studying language in healthy and pathological ageing. It summarizes the state of the art and future research directions of this novel approach.

We present SimpleApprenant, a platform aiming to improve French L2 learners’ knowledge of Multi Word Expressions (MWEs). SimpleApprenant integrates an MWE database annotated with the Common European Framework of Reference for languages (CEFR) level and several Natural Language Processing (NLP) tools: a spelling checker, a parser, and a set of transformation rules. NLP tools and resources are used to build training and writing exercises to improve MWE knowledge and writing skills of French L2 learners. We present the user scenarios, the platform’s architecture, as well as the preliminary evaluation of its NLP tools.

The AI voice assistant mobile application was developed to aid drivers in operating their mobile phones while driving without touching their phones. The literature review examines multiple innovative artificial technologies involved in applications with voice assistants in natural language processing (NLP) techniques. The methodology used involved a qualitative approach, and the design science paradigm was used for the development of the voice assistant for smartphones with NLP techniques. NLP techniques that were applied in the development of the AI voice assistant are smart synthesis, data flow sequence, core and interface accessing, part of speech tagging, named entity recognition, conference resolution, and porter stemming. Some of the operations that are achieved by the application include arithmetic calculations based on voice commands and returning the computer result via voice, searching the internet based on user voice input, and providing a response via voice assistance.

Introduction:Nowadays in Panama, there is a lot of patient information stored in textual form which cannot be manipulated to manage adequate knowledge. There are multiple resources created to represent knowledge, including specialized glossaries, ontologies, among others. The ontologies are an important part within the scope of the recovery and organization of the information and the semantic web. Also in recent works they are used in applications of natural language processing (NLP), as a knowledge base.Aim:This research was conducted with the aim of creating a methodology that allows from a text written in NL, extract the necessary elements using NLP tools and with them create a knowledge base represented by one domain ontology and extract knowledge to help medical specialists.Material and Methods:In this study we carried out a methodology that allows the extraction of knowledge of patient clinical records, general medicine and palliative care, in order to show relevant knowledge elements to specialists. The methodology was validated with a data corpus of approximately 200 patient records.Conclusion:We have created a knowledge representation methodology, combining NLP techniques and tools and the automatic instantiation of an ontology, which can serve as a software agent for other applications or used to visualize the patient’s clinical information. The study was validated using the traditional metrics of information retrieval systems precision, recall, F-measure obtaining excellent results, and can be used as a software agent or methodology for the development of information extraction software systems in the medical domain.

BackgroundQualitative research methods are increasingly being used across disciplines because of their ability to help investigators understand the perspectives of participants in their own words. However, qualitative analysis is a laborious and resource-intensive process. To achieve depth, researchers are limited to smaller sample sizes when analyzing text data. One potential method to address this concern is natural language processing (NLP). Qualitative text analysis involves researchers reading data, assigning code labels, and iteratively developing findings; NLP has the potential to automate part of this process. Unfortunately, little methodological research has been done to compare automatic coding using NLP techniques and qualitative coding, which is critical to establish the viability of NLP as a useful, rigorous analysis procedure.ObjectiveThe purpose of this study was to compare the utility of a traditional qualitative text analysis, an NLP analysis, and an augmented approach that combines qualitative and NLP methods.MethodsWe conducted a 2-arm cross-over experiment to compare qualitative and NLP approaches to analyze data generated through 2 text (short message service) message survey questions, one about prescription drugs and the other about police interactions, sent to youth aged 14-24 years. We randomly assigned a question to each of the 2 experienced qualitative analysis teams for independent coding and analysis before receiving NLP results. A third team separately conducted NLP analysis of the same 2 questions. We examined the results of our analyses to compare (1) the similarity of findings derived, (2) the quality of inferences generated, and (3) the time spent in analysis.ResultsThe qualitative-only analysis for the drug question (n=58) yielded 4 major findings, whereas the NLP analysis yielded 3 findings that missed contextual elements. The qualitative and NLP-augmented analysis was the most comprehensive. For the police question (n=68), the qualitative-only analysis yielded 4 primary findings and the NLP-only analysis yielded 4 slightly different findings. Again, the augmented qualitative and NLP analysis was the most comprehensive and produced the highest quality inferences, increasing our depth of understanding (ie, details and frequencies). In terms of time, the NLP-only approach was quicker than the qualitative-only approach for the drug (120 vs 270 minutes) and police (40 vs 270 minutes) questions. An approach beginning with qualitative analysis followed by qualitative- or NLP-augmented analysis took longer time than that beginning with NLP for both drug (450 vs 240 minutes) and police (390 vs 220 minutes) questions.ConclusionsNLP provides both a foundation to code qualitatively more quickly and a method to validate qualitative findings. NLP methods were able to identify major themes found with traditional qualitative analysis but were not useful in identifying nuances. Traditional qualitative text analysis added important details and context.