A Little Bird Told Me: Mining Tweets for Requirements and Software Evolution

Guzmán, Emitzá; Ibrahim, Mohamed Hamza; Glinz, Martin

doi:10.1109/re.2017.88

Cited by 98 publications

(83 citation statements)

References 25 publications

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“…By crowd [48,54,72], by textual data analysis [13,20,25,33,41,42,49,51,52,62,64,80,86,88,89], by prototyping [22], sentiment analysis [21,79], image and unstructured data analysis [21,73] 22…”

Section: Analysis and Validationmentioning

confidence: 99%

Crowd Intelligence in Requirements Engineering: Current Status and Future Directions

Khan

Liu

Wen

et al. 2019

Lecture Notes in Computer Science

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Software systems are the joint creative products of multiple stakeholders, including both designers and users, based on their perception, knowledge and personal preferences of the application context. The rapid rise in the use of Internet, mobile and social media applications make it even more possible to provide channels to link a large pool of highly diversified and physically distributed designers and end users, the crowd. Converging the knowledge of designers and end users in requirements engineering process is essential for the success of software systems. In this paper, we report the findings of a survey of the literature on crowd-based requirements engineering research. It helps us understand the current research achievements, the areas of concentration, and how requirements related activities can be enhanced by crowd intelligence. Based on the survey, we propose a general research map and suggest the possible future roles of crowd intelligence in requirements engineering.

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Section: Analysis and Validationmentioning

confidence: 99%

Crowd Intelligence in Requirements Engineering: Current Status and Future Directions

Khan

Liu

Wen

et al. 2019

Lecture Notes in Computer Science

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“…Twitter influences many communities including the software engineering community as highlighted by many prior studies [56,9,71,61]. Various techniques have been proposed recently to mine software engineering relevant information from Twitter [52,54,72,23].…”

Section: Knowledge Sources For Software Developersmentioning

confidence: 99%

SIEVE: Helping developers sift wheat from chaff via cross-platform analysis

et al. 2019

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Software developers have benefited from various sources of knowledge such as forums, question-and-answer sites, and social media platforms to help them in various tasks. Extracting software-related knowledge from different platforms involves many challenges. In this paper, we propose an approach to improve the effectiveness of knowledge extraction tasks by performing crossplatform analysis. Our approach is based on transfer representation learning and word embeddings, leveraging information extracted from a source platform which contains rich domain-related content. The information extracted is then used to solve tasks in another platform (considered as target platform) with less domain-related contents. We first build a word embeddings model as a representation learned from the source platform, and use the model to improve the performance of knowledge extraction tasks in the target platform. We experiment with Software Engineering Stack Exchange and Stack Overflow as source platforms, and two different target platforms, i.e., Twitter and YouTube. Our experiments show that our approach improves performance of existing work for the tasks of identifying software-related tweets and helpful YouTube comments.

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“…Conceptually, both studies follow similar goals and structure by: first preprocessing the data; second classifying tweets into their specified categories; and third grouping similar tweets. Guzman et al [14] go one step further and present a weighted function to rank tweets by their relevance. Compared to both papers, we have a strong focus on reporting feature engineering by testing diverse features and feature combinations (see Table II).…”

Section: Related Workmentioning

confidence: 99%

Classifying Multilingual User Feedback using Traditional Machine Learning and Deep Learning

Stanik

Haering

Maalej

2019

2019 IEEE 27th International Requirements Engineering Conference Workshops (REW)

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With the rise of social media like Twitter and of software distribution platforms like app stores, users got various ways to express their opinion about software products. Popular software vendors get user feedback thousandfold per day. Research has shown that such feedback contains valuable information for software development teams such as problem reports or feature and support inquires. Since the manual analysis of user feedback is cumbersome and hard to manage many researchers and tool vendors suggested to use automated analyses based on traditional supervised machine learning approaches. In this work, we compare the results of traditional machine learning and deep learning in classifying user feedback in English and Italian into problem reports, inquiries, and irrelevant. Our results show that using traditional machine learning, we can still achieve comparable results to deep learning, although we collected thousands of labels.

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A Little Bird Told Me: Mining Tweets for Requirements and Software Evolution

Cited by 98 publications

References 25 publications

Crowd Intelligence in Requirements Engineering: Current Status and Future Directions

Crowd Intelligence in Requirements Engineering: Current Status and Future Directions

SIEVE: Helping developers sift wheat from chaff via cross-platform analysis

Classifying Multilingual User Feedback using Traditional Machine Learning and Deep Learning

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