Integrating User Feedback to Enhance Software Quality and User Satisfaction in Mobile Application Development
Notice bibliographique
Résumé
Enhancing software quality and user satisfaction in mobile application (app) development is a multifaceted challenge that requires effective user feedback integration. As mobile apps become increasingly central to users' daily lives, ensuring high quality and user satisfaction is vital for apps aiming to maintain a competitive edge. User feedback serves as a direct channel to understand user needs, preferences, and pain points, making it an invaluable resource for continuous improvement and innovation. However, integrating user feedback into the development process remains challenging due to the sheer volume and unstructured nature of the data. The goal of this thesis is to investigate how user feedback from app reviews can be effectively utilized to improve software quality and user satisfaction. We achieve this through three interconnected studies, each targeting a specific sub-goal.\nThe first study aims to examine whether code quality impacts user feedback. Enhancing the code quality of an app normally contributes to an improvement in the app/software quality. We analyzed nine open-source Android apps using a Mining Software Repository approach. We assessed the impact of code quality indicators—such as code smells, readability, and source code complexity—on user feedback, including ratings, sentiment, and toxicity from Google Play Store reviews. Contrary to our expectations, the analysis did not reveal any statistically significant relationship between code quality and user feedback, highlighting the complexity of this relationship and suggesting the need for further investigation.\nThe second study aims to create a refined classification system for user reviews by correlating them to factors such as code quality, software artifacts, and user sentiment. To achieve this, we employed open coding to categorize issues in app reviews into types such as Crashing, Design, Functionality, and Performance. By connecting these categories with code quality metrics and software artifacts, we sought to gain deeper insights into user feedback. Using machine learning models, we automatically classified issues in reviews from seven open-source Android apps, with the fine-tuned Generative Pre-trained Transformer (GPT-3.5) model achieving the highest accuracy at 95%. This study revealed a statistically significant relationship between the classified issues and both code quality metrics and software artifacts, underscoring the complexity of integrating user feedback into the development process and demonstrating the value of automation for managing and prioritizing large volumes of app reviews.\nGiven the statistically significant relationship identified in the second study, we recognized the need to convey these insights to developers through an efficient and practical tool. Therefore, The third study aims to develop a dashboard designed to streamline user feedback management. This dashboard features the GPT-3.5 model trained in the previous study to detect and categorize issues in user reviews, alongside sentiment and toxicity analysis to gauge user emotions and potential toxic feedback. Additionally, it includes code analysis to identify code smells across different app versions. A survey of app developers was conducted to evaluate the dashboard's usability and effectiveness, indicating positive results. 78% of participants reported that the dashboard effectively helped them manage user feedback and monitor code quality, and 84% indicated that they would recommend it to other developers.\nIn this thesis, we explored the impact of code quality on user feedback, developed a classification system for app reviews, and created a practical tool for developers to provide them with comprehensive insights into effectively utilizing user feedback in app development. Our studies highlight the importance of integrating user feedback through automated classification and real-time sentiment and toxicity analysis, ultimately enhancing software quality and user satisfaction.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,018 | 0,087 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».