Users — The Hidden Software Product Quality Experts?: A Study on How App Users Report Quality Aspects in Online Reviews

Groen, Eduard C.; Kopczyńska, Sylwia; Hauer, Marc P.; Krafft, Tobias D.; Doerr, Joerg

doi:10.1109/re.2017.73

Cited by 43 publications

(48 citation statements)

References 18 publications

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“…For this, we analysed each paper content to identify foci of requirements elicitation and grouped them into categories shown in Table 1. For example, there are works focusing on elicit general requirements, including: building personas for users profiling [3] and identify Personae Non Gratae (potential system attackers or abusers) [51], collecting runtime user feedbacks, or on extraction of novel or emerging functional or nonfunctional requirements [23], such as usability, user experience [4] and awareness [75], or security and privacy requirements [5,8], or building elicitation tools for crowd. Requirements Modeling.…”

mentioning

confidence: 99%

Crowd Intelligence in Requirements Engineering: Current Status and Future Directions

Khan

Liu

Wen

et al. 2019

Requirements Engineering: Foundation for Software Quality

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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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mentioning

confidence: 99%

Crowd Intelligence in Requirements Engineering: Current Status and Future Directions

Khan

Liu

Wen

et al. 2019

Requirements Engineering: Foundation for Software Quality

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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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“…In this context, data-driven requirements engineering [15] is advocated as the proper way to go for eliciting QRs. Some recent proposals in this direction aim at exploiting end-user explicit feedback data [16][17] [18].…”

Section: Background and Related Workmentioning

confidence: 99%

Data-Driven Elicitation of Quality Requirements in Agile Companies

Oriol

Seppänen

Behutiye

et al. 2019

Communications in Computer and Information Science

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Quality Requirements (QRs) are a key artifact to ensure the quality and success of a software system. Despite its importance, QRs have not reached the same degree of attention as its functional counterparts, especially in the context of trending software development methodologies like Agile Software Development (ASD). Moreover, crucial information that can be obtained from data sources of a project under development (e.g. JIRA, github,…) are not fully exploited, or even neglected, in QR elicitation activities. In this work, we present a data-driven approach to semi-automatically generate and document QRs in the context of ASD. We define an architecture focusing on the process and the artefacts involved. We validate and iterate on such architecture by conducting workshops in four companies of different size and profile. Finally, we present the implementation of such architecture, considering the feedback and outcomes of the conducted workshops.

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