Classification of application reviews into software maintenance tasks using data mining techniques

Al-Hawari, Assem; Najadat, Hassan; Shatnawi, Raed

doi:10.1007/s11219-020-09529-8

Cited by 20 publications

(10 citation statements)

References 36 publications

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“…Reason behind choosing BERT: BERT advanced the stateof-the-art for 11 NLP tasks. It achieved an absolute improvement of 7.6% over the previous best score on General Language Understanding Evaluation (GLUE) benchmark 3 ; it 3 https://gluebenchmark.com/ also achieved 93.2% accuracy on SQuAD 1.1 4 , outperforming human performance.…”

Section: Pre-trained Models (Ptm)mentioning

confidence: 92%

“…Stores which can reveal important information about the app for software engineers. App reviews are studied extensively [1] and shown to be a helpful resource for requirement engineering [2], release planning [3], software maintenance [4], change file localization [5], and testing [6]. Filtering informative reviews [7], identifying bugs [8], classifying usability and quality concerns [2], and finding security and privacy issues [9] are among the topics that have been investigated.…”

Section: Introduction Mobile App Users Write About Their Experience O...mentioning

confidence: 99%

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Evaluating Pre-Trained Models for User Feedback Analysis in Software Engineering: A Study on Classification of App-Reviews

Hadi¹,

Fard²

2021

Preprint

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Context: Mobile app reviews written by users on app stores or social media are significant resources for app developers. Analyzing app reviews have proved to be useful for many areas of software engineering (e.g., requirement engineering, testing). Automatic classification of app reviews requires extensive efforts to manually curate a labeled dataset. When the classification purpose changes (e.g. identifying bugs versus usability issues or sentiment), new datasets should be labeled, which prevents the extensibility of the developed models for new desired classes/tasks in practice. Recent pre-trained neural language models (PTM) are trained on large corpora in an unsupervised manner and have found success in solving similar Natural Language Processing problems. However, the applicability of PTMs is not explored for app review classification.Objective: We investigate the benefits of PTMs for app review classification compared to the existing models, as well as the transferability of PTMs in multiple settings.Method: We empirically study the accuracy and time efficiency of PTMs compared to prior approaches using six datasets from literature. In addition, we investigate the performance of the PTMs trained on app reviews (i.e. domain-specific PTMs). We set up different studies to evaluate PTMs in multiple settings: binary vs. multi-class classification, zero-shot classification (when new labels are introduced to the model), multi-task setting, and classification of reviews from different resources. The datasets are manually labeled app review datasets from Google Play Store, Apple App Store, and Twitter data. In all cases, Micro and Macro Precision, Recall, and F1-scores will be used and we will report the time required for training and prediction with the models.

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Section: Pre-trained Models (Ptm)mentioning

confidence: 92%

Section: Introduction Mobile App Users Write About Their Experience O...mentioning

confidence: 99%

Evaluating Pre-Trained Models for User Feedback Analysis in Software Engineering: A Study on Classification of App-Reviews

Hadi¹,

Fard²

2021

Preprint

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“…Dalam kebanyakan kasus, teknik pemeliharaan tidak dapat memberikan solusi yang sebanding dengan biaya pemeliharaan yang dikeluarkan. Dengan kata lain, biaya yang dikeluarkan relatif mahal namun tingkat keberhasilannya kecil [3]. Artinya, masih banyak pemilihan penggunaan teknik pemeliharaan software yang kurang sesuai dengan jenis kasus pemeliharaan yang dihadapi.…”

Section: Pendahuluanunclassified

Semantic Literature Review Pada Survey Teknik Pemeliharaan Software

Fadilah

Wanti

Latifah

2022

jacis

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Teknik pemeliharaan software adalah segala metode yang berhubungan dengan aktivitas untuk menjaga life-cycle sebuah software. Survey yang dilakukan terhadap teknik pemeliharaan software sudah pernah dilakukan oleh peneliti sebelumnya termasuk Lientz, Swanson, dll. Survey kali ini mengangkat latar belakang masalah yang berbeda; yaitu pesatnya perkembangan jenis teknik pemeliharaan yang tidak sebanding dengan kepastian tingkat akurasi, keefektifan penggunaan serta cost yang harus dikeluarkan. Survey yang dimaksud bertujuan untuk memberikan tinjauan komprehensif kepada pembaca tentang kegunaan, kelebihan, kekurangan teknik-teknik pemeliharaan software dalam penelitian kurun waktu 1978-2020. Metode survey yang digunakan adalah systematic literature review terhadap paper significant yang membahas isu terkait. Manfaat yang diharapkan, dapat memberikan pembaca gambaran dan pilihan penggunaan teknik pemeliharaan yang tepat guna untuk masalah pemeliharaan yang mereka hadapi. Survey menghasilkan 24 jenis teknik software. Tujuh diantaranya (reverse engineering, corrective, perfective, adaptive, restructuring, model maintenance, reengineering) masuk ke dalam klasifikasi teknik yang paling sering dibahas dalam penelitian kurun waktu 1978-2020. Dari hasil survey tersebut, didapatkan temuan berupa rekomendasi pilihan teknik pemeliharaan untuk contoh kasus Pembuatan framework maintenance, Pemeliharaan pada aplikasi berbasis web, dan Pemeliharaan Cryptologic System.

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“…Software product line (SPL) engineering supports reusable common software resources by following a predefined architecture and plan. The reuse of different predefined features enables product tailoring to make it fit for customer needs ( Aggarwal & Mani, 2019 ; Camacho et al, 2021 ; Al-Hawari, Najadat & Shatnawi, 2021 ). SPL becomes a vital paradigm for companies as it favors usability, cost, productivity, quality, and time ( Krueger & Clements, 2017 , 2019 ; Chacón-Luna et al, 2019 ; Bolander & Clements, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Towards a component-based system model to improve the quality of highly configurable systems

Abbasi

Hafeez

Asghar

et al. 2022

PeerJ Computer Science

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Due to ever-evolving software developments processes, companies are motivated to develop desired quality products quickly and effectively. Industries are now focusing on the delivery of configurable systems to provide several services to a wide range of customers by making different configurations in a single largest system. Nowadays, component-based systems are highly demanded due to their capability of reusability and restructuring of existing components to develop new systems. Moreover, product line engineering is the major branch of the component-based system for developing a series of systems. Software product line engineering (SPLE) provides the ability to design several software modifications according to customer needs in a cost-effective manner. Researchers are trying to tailor the software product line (SPL) process that integrates agile development technologies to overcome the issues faced during the execution of the SPL process such as delay in product delivery, restriction to requirements change, and exhaustive initial planning. The selection of suitable components, the need for documentation, and tracing back the user requirements in the agile-integrated product line (APL) models still need to improve. Furthermore, configurable systems demand the selected features to be the least dependent. In this paper, a hybrid APL model, quality enhanced application product line engineering (QeAPLE) is proposed that provides support for highly configurable systems (HCS) by evaluating the dependency of features before making the final selection. It also has a documentation and requirement traceability function to ensure that the product meets the desired quality. Two-fold assessments are undertaken to validate the suggested model, with the proposed model being deployed on an active project. After that, we evaluated the proposed model performance and effectiveness using after implementing it in a real-world environment and compared the results with an existing method using statistical analysis. The results of the experimental study proofs that the proposed model is practically and statistically significant as compared to the existing method in terms of effectiveness and participants’ performance. Hence, the statistical results of the comparative analysis show that the proposed model improved ease of understanding and adaptability, required effort, high-quality achievement, and version management are significant i.e., more the 50% as compared to the exiting method i.e., less than 50%. The proposed model offers to assist in the development of a highly configurable system that achieves the needed quality. Therefore, the proposed model manages the variation identification, versions control, components dependency for correct selection of components, and validation activities from domain engineering to application engineering.

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Classification of application reviews into software maintenance tasks using data mining techniques

Cited by 20 publications

References 36 publications

Evaluating Pre-Trained Models for User Feedback Analysis in Software Engineering: A Study on Classification of App-Reviews

Evaluating Pre-Trained Models for User Feedback Analysis in Software Engineering: A Study on Classification of App-Reviews

Semantic Literature Review Pada Survey Teknik Pemeliharaan Software

Towards a component-based system model to improve the quality of highly configurable systems

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