Mining software insights: uncovering the frequently occurring issues in low-rating software applications

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PeerJ Computer Science

Main article text

 

Introduction

  • Curated a novel research dataset of end-user feedback from the ASA store representing frequently occurring issue types in the software apps

  • Proposed novel grounded theory by critically analyzing end-user feedback to identify the frequently occurring issues type UI and UX, functionality and features, compatibility and device-specific, customer support and responsiveness, and security and privacy issues.

  • Developed a truth set for software issues and their types using a content analysis approach

  • Employing a series of fine-tuned baseline ML and DL classifiers such as multinomial naive Bayes (MNB), logistic regression (LR), random forest (RF), multi-layer perception (MLP), k-nearest neighbors (KNN), AdaBoost, Voting, convolutional neural network (CNN), long short-term memory (LSTM), bidirectional long short term memory (BiLSTM), gated recurrent unit (GRU), bidirectional gated recurrent unit (BiGRU), and recurrent neural network (RNN) to report their performance in identifying various issue types.

  • To improve the explainability of ML classifiers (MLP), we employed the SHAP approach to identify the critical features associated with each issue type. It will help software vendors and developers understand the complex decision-making of ML classifiers.

The rest of the article is arranged as follows

Proposed methodology

Proposed research questions

Research method

Research data gathering and development

Grounded theory approach

Manual content analysis

Automated classification

Processing end-user feedback for ml and dl classifiers

User-defined issues and their types

Performance and stability issues

UI/UX issues

Functionality and features issues

Compatibility and device-specific issues

Customer support and responsiveness issues

Security and privacy issues

Labelling user-expressed issues

Annotation process for user-generated issue types

Frequency of frequently occurring issues types in ASA store

Automated classification of end-user feedback into issue types

Experimental setup

Preprocessing

Feature engineering

Data imbalance

Assessment and training

Results

Comparative study

Discussion

User feedback in software development

Analytical framework for software evolution

Implications of identified trends and patterns

Application of ML and DL in feedback analysis

Emphasis on low-rated applications

Challenges and limitations in utilizing user feedback from low-rated applications

Addressing core issues in software applications

Threats to validity

UI/UX issues limitation

Ensure reliability and validity

Generalizability of the findings

Conclusion and future work

Supplemental Information

Experimental Data.

DOI: 10.7717/peerj-cs.2115/supp-1

Additional Information and Declarations

Competing Interests

The authors declare that they have no competing interests.

Author Contributions

Nek Dil Khan conceived and designed the experiments, performed the experiments, analyzed the data, performed the computation work, authored or reviewed drafts of the article, and approved the final draft.

Javed Ali Khan conceived and designed the experiments, performed the experiments, analyzed the data, performed the computation work, authored or reviewed drafts of the article, and approved the final draft.

Jianqiang Li analyzed the data, authored or reviewed drafts of the article, and approved the final draft.

Tahir Ullah performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Qing Zhao performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Data Availability

The following information was supplied regarding data availability:

The data and models are available at GitHub and Zenodo:

- https://github.com/nekdil566/issue-detection.

- Beijing University of Technology, & Khan, N. D. (2024). Mining software insights: uncovering the frequently occurring issues in low-rating software applications. https://doi.org/10.5281/zenodo.11256608.

Funding

The authors received no funding for this work.

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