Data Mining-Based Techniques for Software Fault Localization
원문 초록
This chapter illustrates the basic concepts of fault localization using a data mining technique. It utilizes the Trityp program to illustrate the general method. Formal concept analysis and association rule are two well-known methods for symbolic data mining. In their original inception, they both consider data in the form of an object-attribute table. In their original inception, they both consider data in the form of an object-attribute table. The chapter considers a debugging process in which a program is tested against different test cases. Two attributes, PASS and FAIL, represent the issue of the test case. The chapter extends the analysis of data mining for fault localization for the multiple fault situations. It addresses how data mining can be further applied to fault localization for GUI components. Unlike traditional software, GUI test cases are usually event sequences, and each individual event has a unique corresponding event handler.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Attributes Shape the Embedding Space of Face Recognition Models
Ref-Long: Benchmarking the Long-context Referencing Capability of Long-context Language Models
Emergent Semantics Beyond Token Embeddings: Transformer LMs with Frozen Visual Unicode Representations
MambaFusion: Height-Fidelity Dense Global Fusion for Multi-modal 3D Object Detection
The Trilemma of Truth in Large Language Models
Style-Aligned Image Composition for Robust Detection of Abnormal Cells in Cytopathology
토론
이 논문과 연결된 포럼 글이 아직 없습니다.