Which Non-functional Requirements Do Developers Focus On? An Empirical Study on Stack Overflow Using Topic Analysis

Zou, Jie; Xu, Lan; Guo, Weikang; Meng, Yan; Yang, Dan; Zhang, Xiaohong

doi:10.1109/msr.2015.60

Cited by 35 publications

(30 citation statements)

References 2 publications

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“…Zou et al examined the repository via utilizing the Latent Dirichlet Allocation (LDA) to discover the main topics of discussion. [39]. These topics are related to various NFR which were also identified by the researchers.…”

Section: Related Workmentioning

confidence: 84%

Comparative Study of The Performance of Various Classifiers in Labeling Non-Functional Requirements

Tóth

Vidács

2019

ITC

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Software systems are to be developed based on expectations of customers. These expectations are expressed using natural languages. To design a software meeting the needs of the customer and the stakeholders, the intentions, feedbacks and reviews are to be understood accurately and without ambiguity. These textual inputs often contain inaccuracies, contradictions and are seldom given in a well-structured form. The issues mentioned in the previous thought frequently result in the program not satisfying the expectation of the stakeholders. In particular, for non-functional requirements, clients rarely emphasize these specifications as much as they might be justified. Identifying, classifying and reconciling the requirements is one of the main duty of the System Analyst, which task, without using a proper tool, can be very demanding and time-consuming. Tools which support text processing are expected to improve the accuracy of identification and classification of requirements even in an unstructured set of inputs. System Analysts can use them also in document archeology tasks where many documents, regulations, standards, etc. have to be processed. Methods elaborated in natural language processing and machine learning offer a solid basis, however, their usability and the possibility to improve the performance utilizing the specific knowledge from the domain of the software engineering are to be examined thoroughly. In this paper, we present the results of our work adapting natural language processing and machine learning methods for handling and transforming textual inputs of software development. The major contribution of our work is providing a comparison of the performance and applicability of the state-of-the-art techniques used in natural language processing and machine learning in software engineering. Based on the results of our experiments, tools can be designed which can support System Analysts working on textual inputs.

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Section: Related Workmentioning

confidence: 84%

Comparative Study of The Performance of Various Classifiers in Labeling Non-Functional Requirements

Tóth

Vidács

2019

ITC

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“…Barua et al (2014) conducted the largest study of general trends in software development. Zou et al (2015) examined non-functional requirements. Zou et al, (2017) extended Zou et al (2015).…”

Section: Related Workmentioning

confidence: 99%

“…Zou et al (2015) examined non-functional requirements. Zou et al, (2017) extended Zou et al (2015). Allamanis and Sutton (2013) compared the most common technologies in view of linguistics and semantics.…”

Section: Related Workmentioning

confidence: 99%

Java Geliştiricileri Stack Overflow Üzerinde Ne Hakkında Konuşuyor? Deneysel Bir Çalışma

Şahin¹,

Bayazit²

2020

European Journal of Science and Technology

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Java has been a widely used programming language for a long time in various fields. Java and its libraries have been frequently updated for various reasons including bugs, change requests, performance and usability requirements and so on. In this paper, we examine how these changes affect the use of Java and analyze trends in its usage. As a data source, we use the Stack Overflow public dataset which is the largest online Q&A site about software technologies. We firstly employ a practical approach to extract the Javarelated posts from the Stack Overflow dataset using cosine similarity and compare it with previous works. We then apply Latent Dirichlet Allocation (LDA) to the corpus for topic modelling. We divided the data set into two-year periods to obtain consistent clusters. After obtaining main topics, we examine topics and keywords on a two-year basis. Finally, unlikely previous works, we manually classify topics into two as "domain-specific" and "development environment" and investigate tendencies of these classes to change in both the short term and the long term.

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“…The user interests in social collaborative platforms have been a widely studied research area. There were research work that focused on analyzing topics asked by the user in Stack Overflow [3,4,22,40,44]. Barua et al [4] conducted an extensive empirical study to mine the topics discussed by users in Stack Overflow.…”

Section: Related Workmentioning

confidence: 99%

Wisdom in Sum of Parts

Lee

2018

Proceedings of the 10th ACM Conference on Web Science

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In this paper, we proposed a novel framework which uses user interests inferred from activities (a.k.a., activity interests) in multiple social collaborative platforms to predict users' platform activities. Included in the framework are two prediction approaches: (i) direct platform activity prediction, which predicts a user's activities in a platform using his or her activity interests from the same platform (e.g., predict if a user answers a given Stack Overflow question using the user's interests inferred from his or her prior answer and favorite activities in Stack Overflow), and (ii) cross-platform activity prediction, which predicts a user's activities in a platform using his or her activity interests from another platform (e.g., predict if a user answers a given Stack Overflow question using the user's interests inferred from his or her fork and watch activities in GitHub). To evaluate our proposed method, we conduct prediction experiments on two widely used social collaborative platforms in the software development community: GitHub and Stack Overflow. Our experiments show that combining both direct and cross platform activity prediction approaches yield the best accuracies for predicting user activities in GitHub (AUC=0.75) and Stack Overflow (AUC=0.89).

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Which Non-functional Requirements Do Developers Focus On? An Empirical Study on Stack Overflow Using Topic Analysis

Cited by 35 publications

References 2 publications

Comparative Study of The Performance of Various Classifiers in Labeling Non-Functional Requirements

Comparative Study of The Performance of Various Classifiers in Labeling Non-Functional Requirements

Java Geliştiricileri Stack Overflow Üzerinde Ne Hakkında Konuşuyor? Deneysel Bir Çalışma

Wisdom in Sum of Parts

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