This guide explains what SonarQube is, its advantages, architecture, and provides step‑by‑step Docker installation, project setup, scanner configuration, and real‑world examples of detecting unreachable code and unused parameters.
This article explains why static analysis is essential for C/C++ development, compares compiler warnings with dedicated tools, introduces cppcheck, shows how to install and use it on sample programs, and demonstrates its ability to uncover bugs that compilers often miss.
This article explains the various software defect terms, their classifications, how faults are detected through testing while defects require static analysis, and outlines current challenges and future directions for automated detection and repair of both faults and defects.
This article introduces static code analysis, outlines its advantages and disadvantages, presents eight typical scanning rule categories, and demonstrates common pitfalls such as null‑pointer dereferences, logic errors, uninitialized variables, and potential overflow issues with concrete code examples.
The article outlines essential software testing practices, emphasizing comprehensive business understanding, thorough process knowledge, early and prioritized testing, collaborative test case reviews, post‑test summarization, responsibility, and the benefits of automation to improve quality and efficiency.
The article describes a prototype system that automatically discovers bugs in large distributed databases by instrumenting code, generating massive SQL test cases, statistically analyzing execution paths, visualizing suspicious blocks, and integrating insights from academic papers to guide future debugging and testing efforts.
The Xianyu quality team uses AI—combining a simple CNN, OCR‑LSTM text analysis, and hierarchical image clustering—to automatically spot blank pages, garbled Chinese text, and duplicate screenshots, with an automated retraining pipeline that updates models as the app evolves and plans future widget‑level defect detection.
This article explains static analysis concepts, why cppcheck was chosen for Baidu's testing workflow, details its architecture, and walks through the creation of custom rules—including a printf misuse detector and a dynamic switch misuse rule—while sharing practical challenges and results.
This article explores how deep learning, specifically convolutional neural networks built with Caffe, can be used to automatically detect and predict static UI compatibility bugs such as text overlap in mobile app front‑ends, reducing reliance on manual testing.
