Automatic summarising of user stories in order to be reused in future similar projects

Abstract

User stories play an important role in agile development systems. In this study, a method of summarising user stories is proposed to reuse them in the future. To enhance the results, quality improvement should be made on user stories. It would help developers build better results, and it may also lead to omitting some essential information. To avoid such issues, user stories are duplicated in two exact similar groups, and quality improvement is made on one set while the other set remains unattained. With the help of a modified bag of words and a verb parser, a collection of keywords and key verbs are extracted for both groups. Afterwards, automatic user stories are made, and then an expert improves them. Next, some experts choose between the results and select the better ones. The result is evaluated by applying different experiments on the framework and prototype implementation on 14 data sets of a user story from industry and a fake data set from Duke University. The result showed 97% of micro F ‐measure and 93% of macro F ‐measure, which are promising. These new user stories can be used as the base user stories in future similar projects.

An Extended Survey Concerning the Significance of Artificial Intelligence and Machine Learning Techniques for Bug Triage and Management

“…Moreover, large software repositories determine an interesting scope of scientific research, which is interestingly approached in [26]. Also, the concept of topic modeling is approached in several articles, such as [27], [28], [29], [30], [31], with a clear emphasis on software bug triaging. Moreover, Latent Dirichlet Allocation (LDA) determines an important probability-related algorithmic approach, which is described in paper [32].…”

“…The relevant ontology may be considered for bug triaging during the subsequent phases of the software bug triaging process. Furthermore, the generation of user summaries, which are useful to recommend similar projects during the software development is proposed in article [27], which can be aggregated and used together with other algorithmic model to implement the software bug triaging process. Additionally, another contribution was reported in article [85], which described an approach for the generation of a text summary.…”

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]…”

“…This category aims to identify the key abstractions from NL documents that help analysts understand unknown domains. The key abstraction identification was performed by 16 studies to understand the semantic connection in user stories [48][49][50], identify topics and summarizing user stories [55], [56], construct a goal model from a set of user stories [57], define the ontology for user stories [58], extract the conceptual model of user stories [53,54], prioritize and estimate the user story complexity [56,57], find the linguistic structure of user stories [61], and extract user stories from text [64]- [66].…”

“…Gulle et al [55] identified topics inside crowd-generated user stories using Latent Dirichlet Allocation (LDA), Word Vectors, Word Embeddings, and Word Mover's Distance. In contrast, Resketi et al [56] tried to summarize a set of user stories based on their frequencies.…”