Mining non-functional requirements from App store reviews

Jha, Nishant; Mahmoud, Anas

doi:10.1007/s10664-019-09716-7

Cited by 66 publications

(50 citation statements)

References 65 publications

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“…Guzman et al [18] compared the performance using individual machine learning methods and their ensembles for automatically classifying reviews and found the ensembles can reach a better result. Jha and Mahmoud [19] focused on the non-functional requirements (e.g., security and performance) mining and used classification methods to capture those requirements in reviews. These studies can filter out some noise of app reviews and provide developers with specific types of reviews.…”

Section: App Review Classificationmentioning

confidence: 99%

A Phrase-Level User Requests Mining Approach in Mobile Application Reviews: Concept, Framework, and Operation

Yang

et al. 2021

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Mobile application (app) reviews are feedback about experiences, requirements, and issues raised after users have used the app. The iteration of an app is driven by bug reports and user requirements analyzed and extracted from app reviews, which is a problem that app designers and developers are committed to solving. However, a great number of app reviews vary in quality and reliability. It is a difficult and time-consuming challenge to analyze app reviews using manual methods. To address this, a novel approach is proposed as an automated method to predict high priority user requests with fourteen extracted features. A semi-automated approach is applied to annotate requirements with high or low priority with the help of app changelogs. Reviews from six apps were retrieved from the Apple App Store to evaluate the feasibility of the approach and interpret the principles. The performance comparison results of the approach greatly exceed the IDEA method, with an average precision of 75.4% and recall of 70.4%. Our approach can be applied to specific app development to assist app developers in quickly locating user requirements and implement app maintenance and evolution.

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Section: App Review Classificationmentioning

confidence: 99%

A Phrase-Level User Requests Mining Approach in Mobile Application Reviews: Concept, Framework, and Operation

Yang

et al. 2021

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“…Architects are sometimes involved in the elicitation and definition of quality requirements (Ameller et al 2012;Daneva et al 2013). There are several papers on using different kinds of user reviews on mobile app markets as a potential source of quality requirements (Groen et al 2017;Jha and Mahmoud 2019;Wang et al 2018;Lu and Liang 2017). This is sometimes referred to as CrowdRE (Glinz 2019) or data-driven requirements engineering (Maalej et al 2015).…”

Section: Related Workmentioning

confidence: 99%

A validated model for the scoping process of quality requirements: a multi-case study

2021

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Quality requirements are vital to developing successful software products. However, there exist evidence that quality requirements are managed mostly in an “ad hoc” manner and down-prioritized. This may result in insecure, unstable, slow products, and unhappy customers. We have developed a conceptual model for the scoping process of quality requirements – QREME – and an assessment model – Q-REPM – for companies to benchmark when evaluating and improving their quality requirements practices. Our model balances an upfront forward-loop with a data-driven feedback-loop. Furthermore, it addresses both strategic and operational decisions. We have evaluated the model in a multi-case study at two companies in Sweden and three companies in The Netherlands. We assessed the scoping process practices for quality requirements and provided improvement recommendations for which practices to improve. The study confirms the existence of the constructs underlying QREME. The companies perform, in the median, 24% of the suggested actions in Q-REPM. None of the companies work data-driven with their quality requirements, even though four out of five companies could technically do so. Furthermore, on the strategic level, quality requirements practices are not systematically performed by any of the companies. The conceptual model and assessment model capture a relevant view of the quality requirements practices and offer relevant improvement proposals. However, we believe there is a need for coupling quality requirements practices to internal and external success factors to motive companies to change their ways of working. We also see improvement potential in the area of business intelligence for QREME in selecting data sources and relevant stakeholders.

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“…Extracting concerns at a domain level can be a more challenging problem than focusing on single apps, which typically receive only a limited number of reviews or tweets per day (Mcilroy et al 2017). Furthermore, existing crowd feedback mining techniques are calibrated to extract technical user concerns, such as bug reports and feature requests, often ignoring other non-technical types of concerns that originate from the operational characteristics of the app (Jha and Mahmoud 2019;Martin et al 2017). These observations emphasize the need for new methods that can integrate multiple heterogeneous sources of user feedback to reflect a more accurate picture of the ecosystem.…”

Section: Research Gap and Motivationmentioning

confidence: 99%

“… 2017 ). Furthermore, existing crowd feedback mining techniques are calibrated to extract technical user concerns, such as bug reports and feature requests, often ignoring other non-technical types of concerns that originate from the operational characteristics of the app (Jha and Mahmoud 2019 ; Martin et al. 2017 ).…”

Section: Background Rationale and Research Questionsmentioning

confidence: 99%

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Modeling user concerns in Sharing Economy: the case of food delivery apps

et al. 2020

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Sharing Economy apps, such as Uber, Airbnb, and TaskRabbit, have generated a substantial consumer interest over the past decade. The unique form of peer-to-peer business exchange these apps have enabled has been linked to significant levels of economic growth, helping people in resource-constrained communities to build social capital and move up the economic ladder. However, due to the multidimensional nature of their operational environments, and the lack of effective methods for capturing and describing their end-users' concerns, Sharing Economy apps often struggle to survive. To address these challenges, in this paper, we examine crowd feedback in ecosystems of Sharing Economy apps. Specifically, we present a case study targeting the ecosystem of food delivery apps. Using qualitative analysis methods, we synthesize important user concerns present in the Twitter feeds and app store reviews of these apps. We further propose and intrinsically evaluate an automated procedure for generating a succinct model of these concerns. Our work provides a first step toward building a full understanding of user needs in ecosystems of Sharing Economy apps. Our objective is to provide Sharing Economy app developers with systematic guidelines to help them maximize their market fitness and mitigate their end-users' concerns and optimize their experience.

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Mining non-functional requirements from App store reviews

Cited by 66 publications

References 65 publications

A Phrase-Level User Requests Mining Approach in Mobile Application Reviews: Concept, Framework, and Operation

A Phrase-Level User Requests Mining Approach in Mobile Application Reviews: Concept, Framework, and Operation

A validated model for the scoping process of quality requirements: a multi-case study

Modeling user concerns in Sharing Economy: the case of food delivery apps

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