On the automatic classification of app reviews

Maalej, Walid; Kurtanović, Zijad; Nabil, Hadeer; Stanik, Christoph

doi:10.1007/s00766-016-0251-9

Cited by 224 publications

(203 citation statements)

References 26 publications

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“…Menurut [6], klasfikasi naïve bayes dengan menggunakan binary classification, menghasilkan akurasi yang lebih tinggi daripada metode klasifikasi yang lain, yaitu decision tree dan Max Ent.…”

Section: Pendahuluanunclassified

“…Pada penelitian [4] yang dikembangkan pada penelitian [6], timbal balik pengguna yang berupa ulasan dari toko aplikasi bergerak, dimanfaatkan untuk membantu pengembang dalam meningkatkan kualitas aplikasi bergerak. Selain itu, penelitian [3] juga memanfaatkan timbal balik pengguna yang berupa ulasan dari aplikasi toko bergerak, sebagai rekomendasi untuk pengembang aplikasi, dalam meningkatkan kualitas dalam prses pengembangan aplikasi bergerak.…”

Section: A Keikutsertaan Pengguna Dalam Rekayasa Kebutuhanunclassified

“…Untuk dataset uji, diambil dari [6], dengan jumlah data sebanyak 665. Dataset dengan label bug sebanyak 370, sedangkan dataset dengan label feature sebanyak 295.…”

Section: A Penggalian Dataunclassified

“…Daftar stopword diambil dari [6]. Selain itu, proses penghapusan juga dilakukan pada kata yang tidak dimulai dengan karakter a sampai z (contoh [, ], !, dan %).…”

Section: B Ekstraksi Dataunclassified

“…Dataset yang dipakai untuk tahap pengujian adalah dataset dari [6]. Dataset tersebut terbagi sebanyak empat tipe yaitu: bug, feature, rating, dan user experience.…”

Section: Pengujian Dan Evaluasiunclassified

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Klasifikasi Ulasan Aplikasi Pada Toko Aplikasi Bergerak Dengan Memanfaatkan Issue Tracker Github

Prasetyo¹,

Siahaan²

2017

JUTI

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Section: Pendahuluanunclassified

Section: A Keikutsertaan Pengguna Dalam Rekayasa Kebutuhanunclassified

“…Untuk dataset uji, diambil dari [6], dengan jumlah data sebanyak 665. Dataset dengan label bug sebanyak 370, sedangkan dataset dengan label feature sebanyak 295.…”

Section: A Penggalian Dataunclassified

“…Daftar stopword diambil dari [6]. Selain itu, proses penghapusan juga dilakukan pada kata yang tidak dimulai dengan karakter a sampai z (contoh [, ], !, dan %).…”

Section: B Ekstraksi Dataunclassified

“…Dataset yang dipakai untuk tahap pengujian adalah dataset dari [6]. Dataset tersebut terbagi sebanyak empat tipe yaitu: bug, feature, rating, dan user experience.…”

Section: Pengujian Dan Evaluasiunclassified

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Klasifikasi Ulasan Aplikasi Pada Toko Aplikasi Bergerak Dengan Memanfaatkan Issue Tracker Github

Prasetyo¹,

Siahaan²

2017

JUTI

View full text Add to dashboard Cite

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Requirements elicitation and refinement in collaborative research projects

Angelis

Ferrari

Gnesi

et al. 2018

J Software Evolu Process

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Summary European Union (EU) projects are means of the European Commission for funding research activities. Such projects address challenging research objectives by involving both academic and industrial partners, from several countries. Information and communication technologies–related projects often undertake to deliver a software system prototype. In such a context, most of the typical issues of global requirements engineering may emerge. Partners can have different background and expertise, needs are not sharply defined, and communication is hampered by linguistic and cultural differences. If these issues are not carefully taken into account from the beginning, problems frequently emerge during project execution. This paper presents the experience of applying a customized elicitation and refinement approach in the context of the Learn PAd EU project, which involved about 50 people. The approach combines collaborative elicitation and wiki‐based refinement sessions to come to a set of consolidated requirements. Lessons learnt are discussed as a guidance for researchers dealing with analogous issues in similar contexts. Some of the major observations refer to the importance of initial face‐to‐face meetings when combined with asynchronous remote interactions; the role of moderators that have to encourage collaboration and foster a shared understanding; and the definition of guidelines to select wiki‐based platforms.

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DRIP: Segmenting individual requirements from software requirement documents

Zhao,

Zhang,

Lian

et al. 2023

Softw Pract Exp

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Numerous academic research projects and industrial tasks related to software engineering require individual requirements as input. Unfortunately, according to our observation, several requirements may be packed in one paragraph without explicit boundaries in specification documents. To understand this problem's prevalence, we performed a preliminary study on the open requirement documents widely used in the academic community over the last 10 years, and found that 26% of them include this phenomenon. Several text segmentation approaches have been reported; however, they tend to identify topically coherent units which may contain more than one requirement. What is more, they do not take the constitutions of semantic units of requirements into consideration. Here we report a two‐phase learning‐based approach named DRIP to segment individual requirements from paragraphs. To be specific, we first propose a Requirement Segmentation Siamese framework, which models the similarity of sentences and their conjunction relations, and then detects the initial boundaries between individual requirements. Then, we optimize the boundaries heuristically based on the semantic completeness validation of the segments. Experiments with 1132 paragraphs and 6826 sentences show that DRIP outperforms the popular unsupervised and supervised text segmentation algorithms with respect to processing different documents (with accuracy gains of 57.65%–187.53%) and processing paragraphs of different complexity (with average accuracy gains of 54.46%–158.68%). We also show the importance of each component of DRIP to the segmentation.

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On the automatic classification of app reviews

Cited by 224 publications

References 26 publications

Klasifikasi Ulasan Aplikasi Pada Toko Aplikasi Bergerak Dengan Memanfaatkan Issue Tracker Github

Klasifikasi Ulasan Aplikasi Pada Toko Aplikasi Bergerak Dengan Memanfaatkan Issue Tracker Github

Requirements elicitation and refinement in collaborative research projects

DRIP: Segmenting individual requirements from software requirement documents

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