Mining Twitter Feeds for Software User Requirements

Williams, Grant; Mahmoud, Anas

doi:10.1109/re.2017.14

Cited by 81 publications

(61 citation statements)

References 28 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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“…The algorithm can be separated into three steps: First, it generates a version tree (line 2-7). Then, it processes conversations or single tweets to extract included versions (line [8][9][10][11][12][13][14][15]. Optionally, it resolves existing conflicts (line [16][17][18][19][20][21][22][23][24].…”

Section: B App and System Versionmentioning

confidence: 99%

“…Each leave is marked as an iOS app version. If the list of system version for Android includes the version 3 for l in (l sys−ios , l sys−and , l app−ios , l app−and ) do 4 for version, label in l do 5 version tree.add(version, label) 6 end 7 end process conversation or tweet (2) 8 extracted versions = [] 9 for tweet in c do 10 for token in tweet do 11 potential matches = version tree.match(token, previous token) 12 extracted versions.append(token, potential matches) 13 previous token = token (2) Process Conversation or Tweet. The matcher takes each token including a number and respectively its previous token as input.…”

Section: B App and System Versionmentioning

confidence: 99%

Extracting and Analyzing Context Information in User-Support Conversations on Twitter

Martens

Maalej

2019

2019 IEEE 27th International Requirements Engineering Conference (RE)

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While many apps include built-in options to report bugs or request features, users still provide an increasing amount of feedback via social media, like Twitter. Compared to traditional issue trackers, the reporting process in social media is unstructured and the feedback often lacks basic context information, such as the app version or the device concerned when experiencing the issue. To make this feedback actionable to developers, support teams engage in recurring, effortful conversations with app users to clarify missing context items.This paper introduces a simple approach that accurately extracts basic context information from unstructured, informal user feedback on mobile apps, including the platform, device, app version, and system version. Evaluated against a truthset of 3014 tweets from official Twitter support accounts of the 3 popular apps Netflix, Snapchat, and Spotify, our approach achieved precisions from 81% to 99% and recalls from 86% to 98% for the different context item types. Combined with a chatbot that automatically requests missing context items from reporting users, our approach aims at auto-populating issue trackers with structured bug reports.

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Mining Twitter Feeds for Software User Requirements

Cited by 81 publications

References 28 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

Extracting and Analyzing Context Information in User-Support Conversations on Twitter

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