Characterizing User Decision based on Argumentative Reviews

Kunaefi, Anang; Aritsugi, Masayoshi

doi:10.1109/bdcat50828.2020.00002

Cited by 6 publications

(11 citation statements)

References 24 publications

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“…Another key use of the classification task is rationale mining; it involves detecting types of argumentations and justification users describe in reviews when making certain decisions, e.g. about upgrading, installing, or switching apps (Kurtanović and Maalej 2017;Kurtanovic and Maalej 2018;Kunaefi and Aritsugi 2020).…”

Section: Classificationmentioning

confidence: 99%

“…user interface (UI) design(Alqahtani and Orji 2019;Sharma and Bashir 2020;Franzmann et al 2020) and capturing design rationale(Groen et al 2017;Kurtanović and Maalej 2017;Kurtanovic and Maalej 2018;Jha and Mahmoud 2019;Kunaefi and Aritsugi 2020).…”

mentioning

confidence: 99%

“…al. 2017;Kurtanović and Maalej 2017;Kurtanovic and Maalej 2018;Jha and Mahmoud 2019;Kunaefi and Aritsugi 2020). Kurtanović and Maalej devised a grounded theory for gathering user rationale and evaluated different review classification approaches to mine the information from app reviews(Kurtanović and Maalej 2017;Kurtanovic and Maalej 2018).…”

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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: Classificationmentioning

confidence: 99%

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confidence: 99%

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Analysing app reviews for software engineering: a systematic literature review

et al. 2022

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“…Finally, extracting the correlation between user decisions and their justification can provide insights into which app functionalities/features lead to certain user decisions. For example, in a fitness tracking app, some users might purchase the app because of its sleep pattern feature, while other users rate the app because of its heart rate detection feature [24] (see Fig. 1).…”

Section: Nomentioning

confidence: 99%

“…Kurtanovic and Maalej [23] conducted a grounded theory study involving experts in the field of software engineering and described the decision concept as an action that has already been taken or will be taken by a user; the decision concept comprises four actions, i.e., acquiring, updating, switching and relinquishing an app. Kunaefi and Aritsugi [24], on the other hand, utilized an unsupervised learning approach with latent dirichlet allocation (LDA) [25] on argumentative reviews to specify various user decisions (i.e., acquiring, buying, recommendation, requesting, and 1) Acquiring Decision is an action expressed by users indicating that the users are willing to acquire, purchase, and use the app. For example, a review "The app works without any glitches.…”

Section: A Problem Definitionmentioning

confidence: 99%

Extracting Arguments Based on User Decisions in App Reviews

Kunaefi

Aritsugi

2021

IEEE Access

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Review mining from app marketplaces has gained immense popularity from researchers in recent years. Most studies in this area, however, tend to focus on improving the performance of classification prediction. In this study, we consider review mining from a different perspective, that is, mining user actions/decisions along with their respective arguments/reasons. Our motivation is to obtain a deeper understanding of users' decisions regarding applications and their underlying justifications, e.g., why users give ratings or recommendations. These information abstractions can benefit app developers, especially in planning app updates, by providing data-driven requirements from users' points of view. We utilized a supervised learning approach and built a machine-based annotator to set the ground truth. Seven classifiers and different feature configurations were trained and evaluated on two app review datasets. We then extracted relations between user decisions and arguments based on functional and nonfunctional requirement attributes. The results show an improved performance over the results of the baselines and favorably acceptable performance compared to the results from a human assessment.

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How are decisions made in open source software communities? — Uncovering rationale from python email repositories

Sharma

Savarimuthu

Stanger

2023

J Software Evolu Process

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Group decision‐making (GDM) processes shape the evolution of open source software (OSS) products, thus playing an important role in the governance of open source software communities. While these GDM processes have attracted the attention of researchers, the rationale behind decisions, that is, how decisions are made that enhance the OSS, have not received much attention. This work bridges this gap by extracting these rationales from a large open source repository comprising 1.55 million emails available in Python development archives. This work makes a methodological contribution by presenting a heuristics‐based rationale extraction system called Rationale Miner that employs information retrieval, natural language processing, and heuristics‐based techniques. Using these techniques, it extracts the rationale behind specific decisions (for example, whether a new module was added based on core developer consensus or a benevolent dictator's pronouncement). This work unearths 11 such rationales behind decisions in the Python community and thus makes a knowledge contribution. It also analyzes the prevalence of these rationales across all PEPs and three sub‐types of PEPs: Process, Informational, and Standard Track PEPs. The effectiveness of our contributions has been positively evaluated using quantitative and qualitative approaches (e.g., comparison against baselines for rationale identification showed up to 47% improvement in the most conservative case, and feedback from the Python steering committee showed the accurate identification of rationales respectively). The approach proposed in this work can be used and extended to discover the rationale behind decisions that remain hidden in communication repositories of other OSS projects, which will make the decision‐making (DM) process transparent to stakeholders and encourage decision‐makers to be more accountable.

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Characterizing User Decision based on Argumentative Reviews

Cited by 6 publications

References 24 publications

Analysing app reviews for software engineering: a systematic literature review

Analysing app reviews for software engineering: a systematic literature review

Extracting Arguments Based on User Decisions in App Reviews

How are decisions made in open source software communities? — Uncovering rationale from python email repositories

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