App Store Effects on Software Engineering Practices

Al-Subaihin, Afnan A.; Sarro, Federica; Black, Sue; Capra, Licia; Harman, Mark

doi:10.1109/tse.2019.2891715

Cited by 52 publications

(63 citation statements)

References 91 publications

(100 reference statements)

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“…In this paper we empirically observe how five textual feature extraction techniques perform when used as clustering input, applied on the case of mobile app descriptions. We specifically select the baseline techniques that are most commonly used in Software Engineering research as reported by Arnaoudova et al (2015) and discussed in Section 5.…”

Section: Text Representation Techniquesmentioning

confidence: 99%

“…Extracting features is also useful for the task of tackling and analysing user reviews. This aims to guide developers in sorting app reviews into bug reports and/or feature requests (or a variation of this taxonomy) which developers believe can be challenging (AlSubaihin et al 2019). Research also looked into collating user feedback into specific issues/features to help prioritise and summarise the large amount of feedback.…”

Section: Apps' Feature Extraction From Artefacts Written In Natural Lmentioning

confidence: 99%

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Empirical comparison of text-based mobile apps similarity measurement techniques

et al. 2019

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Context Code-free software similarity detection techniques have been used to support different software engineering tasks, including clustering mobile applications (apps). The way of measuring similarity may affect both the efficiency and quality of clustering solutions. However, there has been no previous comparative study of feature extraction methods used to guide mobile app clustering. Objective In this paper, we investigate different techniques to compute the similarity of apps based on their textual descriptions and evaluate their effectiveness using hierarchical agglomerative clustering. Method To this end we carry out an empirical study comparing five different techniques, based on topic modelling and keyword feature extraction, to cluster 12,664 apps randomly sampled from the Google Play App Store. The comparison is based on three main criteria: silhouette width measure, human judgement and execution time. Results The results of our study show that using topic modelling, in addition to collocationbased and dependency-based feature extractors perform similarly in detecting app-feature similarity. However, dependency-based feature extraction performs better than any other in finding application domain similarity (ρ = 0.7, p − value < 0.01).

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Section: Text Representation Techniquesmentioning

confidence: 99%

Section: Apps' Feature Extraction From Artefacts Written In Natural Lmentioning

confidence: 99%

Empirical comparison of text-based mobile apps similarity measurement techniques

et al. 2019

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“…Harman et al introduces two relevant topics: app store analysis app testing and optimisation [9], yet does not discuss possible relationships between these topics. Sibaihin et al discusses alpha and beta testing by humans using facilities provided by the app store [1]; their paper does not cover pre-launch testing, other release management tools, or Android Vitals.…”

Section: Related Workmentioning

confidence: 99%

“…As figure 1 shows, Android Vitals collects usage and run-time data from Android devices running Google Mobile Services 1 , from users who agreed to provide diagnostic data. It is available to Android developers who have active apps in Google Play; these developers can provide granular access to other people who have a valid Google Account.…”

Section: Introductionmentioning

confidence: 99%

Better Android apps using Android vitals

Harty¹,

Müller

2019

Proceedings of the 3rd ACM SIGSOFT International Workshop on App Market Analytics

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Google provides Android Vitals, a set of reports and tools for Android Developers as part of Google Play Console. Android Vitals can help developers improve their Android apps after an app has been launched by providing information on how their app is performing in key areas such as battery use, performance, and stability (freezes and crashes). Android Vitals also provides various comparisons, including against global bad behavior thresholds, against various peer groups of apps, and across releases of this app. Developers confirm Android Vitals notifies them of relevant problems and they found it valuable even if they also use crash reporting and mobile analytics. The underlying data is used by Google to assess the relative quality of Android apps; and the perceived quality may materially affect the visibility of an app in the Google Play Store. Yet little is known about the tools. This paper outlines various experiences from the developers' perspective of using Android Vitals with several popular Android apps to help open discussions and suggest further research areas. It introduces an open source project, created as part of our work, that enables developers to download pertinent data, particularly crash reports. The data can be analysed both by the development team and others. A particular benefit of this tool is to make the data available outside of the Google platform, which allows developers and (indirectly) researchers to develop additional analysis techniques not currently provided by the platform. CCS CONCEPTS • Software and its engineering → Application specific development environments; Software testing and debugging; Empirical software validation.

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“…App reviews is a rich source of user opinions [1,2,14,17]. These opinions can help developers to understand how users perceive their app, what are users' requirements, or what are users' preferences [1,2,14]. Not surprisingly, knowing user opinions is an important information need developers seek to satisfy [2,4].…”

Section: Introductionmentioning

confidence: 99%

Mining User Opinions to Support Requirement Engineering: An Empirical Study

Dąbrowski

Letier

Perini

et al. 2020

Lecture Notes in Computer Science

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App reviews provide a rich source of user opinions that can support requirement engineering activities. Analysing them manually to find these opinions, however, is challenging due to their large quantity and noisy nature. To overcome the problem, automated approaches have been proposed for so-called opinion mining. These approaches facilitate the analysis by extracting features discussed in app reviews and identifying their associated sentiments. The effectiveness of these approaches has been evaluated using different methods and datasets. Unfortunately, replicating these studies to confirm their results and to provide benchmarks of different approaches is a challenging problem. We address the problem by extending previous evaluations and performing a comparison of these approaches. In this paper, we present an empirical study in which, we evaluated feature extraction and sentiment analysis approaches on the same dataset. The results show these approaches achieve lower effectiveness than reported originally, and raise an important question about their practical use.

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App Store Effects on Software Engineering Practices

Cited by 52 publications

References 91 publications

Empirical comparison of text-based mobile apps similarity measurement techniques

Empirical comparison of text-based mobile apps similarity measurement techniques

Better Android apps using Android vitals

Mining User Opinions to Support Requirement Engineering: An Empirical Study

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