Studying the consistency of star ratings and the complaints in 1 &amp; 2-star user reviews for top free cross-platform Android and iOS apps

Hu, Hanyang; Bezemer, Cor‐Paul; Hassan, Ahmed E.

doi:10.7287/peerj.preprints.2589

Cited by 9 publications

(12 citation statements)

References 21 publications

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“…We observed an interest in use of descriptive and inferential techniques for Content Analysis e.g., Vasa et al (2012), Pagano and Maalej (2013), Mercado et al (2016), Guzman et al (2018), and Wang et al (2020a). Summary statistics, box plots, and cumulative distribution charts help to gain understanding of review characteristics like their vocabulary size (Hoon et al 2012;Vasa et al 2012), issue type distribution (McIlroy et al 2016;Hu et al 2018;Williams et al 2020), or topics these reviews convey (Pagano and Maalej 2013;Srisopha and Alfayez 2018). Scholars employ different statistical tests to test check their hypothesis (Khalid et al 2016;Guzman and Paredes Rojas 2019;Franzmann et al 2020), to examine relationship between reviews characteristics (Srisopha and Alfayez 2018; Guzman and Paredes Rojas 2019; Di Sorbo et al 2020), and to study how sampling bias affects the validity of research results (Martin et al 2015).…”

Section: Discussionmentioning

confidence: 99%

“…Interestingly, studies have pointed out that users' perception for the same apps can vary per country (Srisopha et al 2019), user gender (Guzman and Paredes Rojas 2019), development framework (Malavolta et al 2015a), and app store (Ali et al 2017). Content analysis can be also beneficial for software engineers to understand whether cross-platform apps achieve consistency of users' perceptions across different app stores (Hu et al 2018;Hu et al 2019), or whether hybrid development tools achieve their main purpose: delivering an app that is perceived similarly by users across platforms (Hu et al 2019). Finally, studying the dialogue between users and developers has shown evidences that the chances of users to update their rating for an app increase as result of developer's response to reviews (McIlroy et al 2015;.…”

Section: Content Analysismentioning

confidence: 99%

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Analysing app reviews for software engineering: a systematic literature review

et al. 2022

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App reviews found in app stores can provide critically valuable information to help software engineers understand user requirements and to design, debug, and evolve software products. Over the last ten years, a vast amount of research has been produced to study what useful information might be found in app reviews, and how to mine and organise such information as efficiently as possible. This paper presents a comprehensive survey of this research, covering 182 papers published between 2012 and 2020. This survey classifies app review analysis not only in terms of mined information and applied data mining techniques but also, and most importantly, in terms of supported software engineering activities. The survey also reports on the quality and results of empirical evaluation of existing techniques and identifies important avenues for further research. This survey can be of interest to researchers and commercial organisations developing app review analysis techniques and to software engineers considering to use app review analysis.

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Section: Discussionmentioning

confidence: 99%

Section: Content Analysismentioning

confidence: 99%

Analysing app reviews for software engineering: a systematic literature review

et al. 2022

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“…We use Wilcoxon Mann-Whitney U test to determine whether the difference between any two settings for a given variable is statistically significant (i.e, 𝛼 ≤ 0.05). Mann-Whitney U test is non-parametric, which is used in software engineering literature to compare data that is not normally distributed [31]. We estimate the effect size between the distributions.…”

Section: Results From Coding Tasksmentioning

confidence: 99%

Automatic API Usage Scenario Documentation from Technical Q&A Sites

Uddin¹,

Khomh²,

Roy³

2021

Preprint

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The online technical Q&A site Stack Overflow (SO) is popular among developers to support their coding and diverse development needs. To address shortcomings in API official documentation resources, several research has thus focused on augmenting official API documentation with insights (e.g., code examples) from SO. The techniques propose to add code examples/insights about APIs into its official documentation. Recently, surveys of software developers find that developers in SO consider the combination of code examples and reviews about APIs as a form of API documentation, and that they consider such a combination to be more useful than official API documentation when the official resources can be incomplete, ambiguous, incorrect, and outdated. Reviews are opinionated sentences with positive/negative sentiments. However, we are aware of no previous research that attempts to automatically produce API documentation from SO by considering both API code examples and reviews. In this paper, we present two novel algorithms that can be used to automatically produce API documentation from SO by combining code examples and reviews towards those examples.The first algorithm is called statistical documentation, which shows the distribution of positivity and negativity around the code examples of an API using different metrics (e.g., star ratings). The second algorithm is called concept-based documentation, which clusters similar and conceptually relevant usage scenarios. An API usage scenario contains a code example, a textual description of the underlying task addressed by the code example, and the reviews (i.e., opinions with positive and negative sentiments) from other developers towards the code example. We deployed the algorithms in Opiner, a web-based platform to aggregate information about APIs from online forums. We evaluated the algorithms by mining all Java JSON-based posts in SO and by conducting three user studies based on produced documentation from the posts. The first study is a survey, where we asked the participants to compare our proposed algorithms against a Javadoc-syle documentation format (called as Type-based documentation in Opiner). The participants were asked to compare along four development scenarios (e.g., selection, documentation). The participants preferred our proposed two algorithms over type-based documentation. In our second user study, we asked the participants to complete four coding tasks using Opiner and the API official and informal documentation resources. The participants were more effective and accurate while using Opiner. In a subsequent survey, more than 80% of participants asked the Opiner documentation platform to be integrated into the formal API documentation to complement and improve the API official documentation.CCS Concepts: • Software and its engineering → Software creation and management; Documentation; Searchbased software engineering.

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“…Since the range for the number of reviews was large, we used a logarithmic scale in the y-axis to aid differentiating apps in this dimension. Although apps generally tend to get better ratings on Android than in the iOS platform [34,35], gamified fitness tracker apps seemed to be breaking this trend as they were more favourably rated in the iOS platform than in the Android platform as shown in the left part of Figure 2. Our final dataset has been previously published and is freely available via an openaccess creative commons license [36].…”

Section: Overview Of Game Datamentioning

confidence: 99%

The Role of Steps and Game Elements in Gamified Fitness Tracker Apps: A Systematic Review

Neupane

Hansen

Fails

et al. 2021

MTI

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This article reviews 103 gamified fitness tracker apps (Android and iOS) that incorporate step count data into gameplay. Games are labeled with a set of 13 game elements as well as meta-data from the app stores (e.g., avg rating, number of reviews). Network clustering and visualizations are used to identify the relationship between game elements that occur in the same games. A taxonomy of how steps are used as rewards is provided, along with example games. An existing taxonomy of how games use currency is also mapped to step-based games. We show that many games use the triad of Social Influence, Competition, and Challenges, with Social Influence being the most common game element. We also identify holes in the design space, such as games that include a Plot element (e.g., Collaboration and Plot only co-occur in one game). Games that use Real-Life Incentives (e.g., allow you to translate steps into dollars or discounts) were surprisingly common, but relatively simple in their gameplay. We differentiate between task-contingent rewards (including completion-contingent and engagement-contingent) and performance-contingent rewards, illustrating the differences with fitness apps. We also demonstrate the value of treating steps as currency by mapping an existing currency-based taxonomy onto step-based games and providing illustrations of nine different categories.

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Studying the consistency of star ratings and the complaints in 1 & 2-star user reviews for top free cross-platform Android and iOS apps

Cited by 9 publications

References 21 publications

Analysing app reviews for software engineering: a systematic literature review

Analysing app reviews for software engineering: a systematic literature review

Automatic API Usage Scenario Documentation from Technical Q&A Sites

The Role of Steps and Game Elements in Gamified Fitness Tracker Apps: A Systematic Review

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