What Quality Attributes Can We Find in Product Backlogs? A Machine Learning Perspective

User Stories and Natural Language Processing: A Systematic Literature Review

“…Some studies were authored/co-authored by the same person, indicating the existence of an active research group in this field. [33] Identify ambiguous user stories [34] Define and measure quality factors from user stories [4], [35] Obtain a security defect reporting form from user stories [36] Indicate duplication between user stories [37] Generate model/artifact Generate a test case from user stories [38]- [43] Generate a class diagram from user stories [44], [45] Generate a sequence diagram from user stories [46] Generate a use case diagram from user stories [47]- [49] Generate a use case scenario from user stories [50] Generate a multi-agent system from user stories [51] Generate a source code from user stories [40] Generate a BPMN diagram from user stories [40] Identify the key abstractions To understand the semantic connection in user stories [52]- [54] Identify topics and summarizing user stories [55], [56] Construct a goal model from a set of user stories. [57] Define ontology for user stories [58] Extract the conceptual model of user stories [59], [60] To find the linguistic structure of user stories [61] Prioritizing and estimation of user story complexity [62], [63] Extracting user stories from text [64]- [66] Trace links between model/NL requirements Tracking the development status of user stories from software artifacts [67] Identify the type of dependency of user stories [68] Traceability user stories and software artifact [69]…”

“…Five studies reported methods for finding defects or improving the quality of user stories. The category is meant to serve four purposes: (a) providing recommendations on incomplete requirements based on the knowledge gap [33]; (b) identifying ambiguous user stories [34]; (c) defining and measuring quality factors from user stories [4], [35]; (d) obtaining a security defect reporting form from the user stories [36] and (e) indicating duplications between user stories [37]. Bäumer and Geierhos [33] identified incomplete requirements with preprocessing, lemmatization, and POS tagging.…”

“…Galster et al [35] identified quality attributes in user stories categorized according to their quality attributes (i.e., compatibility, maintainability, performance, portability, reliability, and security). Lucassen et al [4] defined the user story quality.…”

“…The user story can be either input or output of the generated artifact. Fourteen studies reported methods for generating software model/artifacts from user stories, that is, generating a test case from user stories [35][36][37][38][39]64], generating class diagrams from user stories [40,41], generating sequence diagrams from user stories [46], generating a use case diagram from user stories [43][44][45], generating a use case scenario from user stories [50], generating a multi-agent system from user stories [51], generating a source code from user stories [40], and generating BPMN diagrams from user stories [40]. The software artifact generation aims to cut time and cost in software development and avoid inconsistencies, incompleteness, and incorrect requirements and artifact/software models.…”

A Systematic Literature Review on Using Machine Learning Algorithms for Software Requirements Identification on Stack Overflow

“…Normally software requirements are of two types, namely, FRs and NFRs. e research work on the difference between FRs and NFRs is defined and well known; however, the automatic identification and classification of the software requirements stated in different natural language is still a huge challenge [14][15][16][17][18][19][20][21].…”