Using app reviews for competitive analysis: tool support

Shah, Faheem; Sirts, Kairit; Pfahl, Dietmar

doi:10.1145/3340496.3342756

Cited by 18 publications

(14 citation statements)

References 14 publications

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“…can give an overall idea about the main problems, in particular if compared against other apps (e.g., 'Do users complain more about security of my app compared to similar apps?'). Similarly, analyzing the evolution of user opinions and bug reports about specific features can help software engineers monitor the health of these features and to prioritize maintenance tasks (Vu et al 2015a;Vu et al 2016;Bakiu and Guzman 2017;Shah et al 2019c). For instance, software engineers can analyse how often negative opinions emerge, for how long these opinions have been reported, and whether their frequency is rising or declining (Vu et al 2015a;Gu and Kim 2015;Tao et al 2020).…”

Section: Visualizationmentioning

confidence: 99%

“…Software engineers can make use of the reviews to elicit new requirements (AlSubaihin et al 2019;Dalpiaz and Parente 2019;Da ¸browski et al 2019;. For instance, they can employ opinion mining approaches to examine reviews talking negatively about app features (Guzman and Maalej 2014;Shah et al 2016;Li et al 2018;Shah et al 2019c;Liu et al 2019;Dalpiaz and Parente 2019;Da ¸browski et al 2019;. This can help developers to understand user concerns about problematic features, and potentially help eliciting new requirements (Johann et al 2017;Dalpiaz and Parente 2019;Da ¸browski et al 2019;.…”

Section: Requirements Elicitationmentioning

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: Visualizationmentioning

confidence: 99%

Section: Requirements Elicitationmentioning

confidence: 99%

Analysing app reviews for software engineering: a systematic literature review

et al. 2022

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“…Valuing the idea of Dalpiaz and Parente, some researchers also conducted competitive analyses of mobile apps using online user reviews. Shah et al [45] developed a tool, called RevSUM, that can automatically extract the most relevant features from user reviews for an app developer. Assi et al [46] proposed a review analysis method, called FeatCompare, that can automatically identify high-level features by comparing user reviews among competing apps.…”

Section: Discussionmentioning

confidence: 99%

Improving RE-SWOT Analysis with Sentiment Classification: A Case Study of Travel Agencies

Hsu

2021

Future Internet

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Nowadays, many companies collect online user reviews to determine how users evaluate their products. Dalpiaz and Parente proposed the RE-SWOT method to automatically generate a SWOT matrix based on online user reviews. The SWOT matrix is an important basis for a company to perform competitive analysis; therefore, RE-SWOT is a very helpful tool for organizations. Dalpiaz and Parente calculated feature performance scores based on user reviews and ratings to generate the SWOT matrix. However, the authors did not propose a solution for situations when user ratings are not available. Unfortunately, it is not uncommon for forums to only have user reviews but no user ratings. In this paper, sentiment analysis is used to deal with the situation where user ratings are not available. We also use KKday, a start-up online travel agency in Taiwan as an example to demonstrate how to use the proposed method to build a SWOT matrix.

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“…With such numerous Apps, the number of Apps with similar functions will also increase continually, and they will satisfy similar user requirements and share a common user group. Thus, these Apps will generate a fiercely competitive relationship with each other [2,3]. Take the APKPure as an example, which is a website like Google Play that allows users to download '.apk' files.…”

Section: Introductionmentioning

confidence: 99%

Mining detailed information from the description for App functions comparison

et al. 2021

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The rapid development of Apps not only brings huge economic benefit but also causes increasingly fierce competition. In such a situation, developers are required to develop and update innovative functions to attract and retain users. Afterwards, analysing the functions of similar products can help developers formulate a well-designed plan at the beginning of development as well as make updated strategies during the version update process. However, although there have been some methods that can be applied to extract the features from App descriptions to achieve this purpose to some extent, the features they obtained do not cover the details of App functions. Therefore, to conduct an in-depth research on App functions, a novel method is proposed to extract App features with detailed information and an approach to integrate the gained results for further helping developers obtain the valuable knowledge better is provided. Subsequently, a series of experiments is carried out to evaluate our method. The results reveal that the proposed method can mine the features with detailed information from descriptions and integrate them effectively and also can assist developers to compare with other competitors and develop a better competitive analysis scheme.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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Using app reviews for competitive analysis: tool support

Cited by 18 publications

References 14 publications

Analysing app reviews for software engineering: a systematic literature review

Analysing app reviews for software engineering: a systematic literature review

Improving RE-SWOT Analysis with Sentiment Classification: A Case Study of Travel Agencies

Mining detailed information from the description for App functions comparison

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