[go: up one dir, main page]

Siddique et al., 2024 - Google Patents

Hybrid framework to exclude similar and faulty test cases in regression testing

Siddique et al., 2024

View PDF
Document ID
8519678722919447272
Author
Siddique M
Wan-Kadir W
Ahmad J
Ibrahim N
Publication year
Publication venue
Baghdad Science Journal

External Links

Snippet

Regression testing is a crucial phase in the software development lifecycle that makes sure that new changes/updates in the software system don't introduce defects or don't affect adversely the existing functionalities. However, as the software systems grow in complexity …
Continue reading at bsj-ojs.uobaghdad.edu.iq (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/36—Preventing errors by testing or debugging software
    • G06F11/3668—Software testing
    • G06F11/3672—Test management
    • G06F11/3676—Test management for coverage analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/07—Error detection; Error correction; Monitoring responding to the occurence of a fault, e.g. fault tolerance
    • G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
    • G06F11/0766—Error or fault reporting or storing
    • G06F11/0775—Content or structure details of the error report, e.g. specific table structure, specific error fields
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/07—Error detection; Error correction; Monitoring responding to the occurence of a fault, e.g. fault tolerance
    • G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
    • G06F11/0706—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation the processing taking place on a specific hardware platform or in a specific software environment
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/07—Error detection; Error correction; Monitoring responding to the occurence of a fault, e.g. fault tolerance
    • G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
    • G06F11/079—Root cause analysis, i.e. error or fault diagnosis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/36—Preventing errors by testing or debugging software
    • G06F11/3604—Software analysis for verifying properties of programs
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/22—Detection or location of defective computer hardware by testing during standby operation or during idle time, e.g. start-up testing
    • G06F11/2257—Detection or location of defective computer hardware by testing during standby operation or during idle time, e.g. start-up testing using expert systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063—Operations research or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/02—Knowledge representation
    • G06N5/022—Knowledge engineering, knowledge acquisition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50—Computer-aided design
    • G06F17/5009—Computer-aided design using simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computer systems based on specific mathematical models
    • G06N7/005—Probabilistic networks
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere

Similar Documents

Publication Publication Date Title
Yang et al. Towards semi-automatic bug triage and severity prediction based on topic model and multi-feature of bug reports
Hu et al. Effective bug triage based on historical bug-fix information
Shatnawi A quantitative investigation of the acceptable risk levels of object-oriented metrics in open-source systems
Lugaresi et al. Online validation of digital twins for manufacturing systems
CN106201871A (en) Based on the Software Defects Predict Methods that cost-sensitive is semi-supervised
US20160342720A1 (en) Method, system, and computer program for identifying design revisions in hardware design debugging
Najafi et al. Bisecting commits and modeling commit risk during testing
CN107066389A (en) The Forecasting Methodology that software defect based on integrated study is reopened
Zhang et al. FENSE: A feature-based ensemble modeling approach to cross-project just-in-time defect prediction
Ye et al. Knowledge discovery and knowledge transfer in board-level functional fault diagnosis
Siddique et al. Hybrid framework to exclude similar and faulty test cases in regression testing
Ni et al. Unifying defect prediction, categorization, and repair by multi-task deep learning
Singh et al. Cross company and within company fault prediction using object oriented metrics
Duque-Torres et al. Exploring a test data-driven method for selecting and constraining metamorphic relations
US10546080B1 (en) Method and system for identifying potential causes of failure in simulation runs using machine learning
Pal et al. A classification of software defect prediction models
Arani et al. SoK: Machine learning for continuous integration
Ngo et al. Ranking warnings of static analysis tools using representation learning
Laghmouch et al. Classifying process deviations with weak supervision
Kutlubay et al. A two-step model for defect density estimation
Wong et al. An approach towards designing a prescriptive analytical logic model for software application root cause analysis
Parsa et al. Finding causes of software failure using ridge regression and association rule generation methods
Alhazzaa et al. A systematic mapping study on software reliability growth models that consider evolution
Mei et al. Deriving Thresholds of Object-Oriented Metrics to Predict Defect-Proneness of Classes: A Large-Scale Meta-Analysis
Yadav et al. Hybrid model for software fault prediction