[go: up one dir, main page]

John et al., 2021 - Google Patents

Test case Recommendation for regression with Named Entity Recognition for test step prediction

John et al., 2021

Document ID
16475320796634124280
Author
John S
Gaur D
Siddiqui A
Publication year
Publication venue
2021 4th Biennial International Conference on Nascent Technologies in Engineering (ICNTE)

External Links

Snippet

Regression testing plays an important role in controlling the quality of the software. It, in general, accounts for a large proportion of software development costs. Therefore, if it is possible to establish any underlying relations in the existing test data, through automatic test …
Continue reading at ieeexplore.ieee.org (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
    • 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
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6279—Classification techniques relating to the number of classes
    • 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
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F17/30705—Clustering or classification
    • G06F17/3071—Clustering or classification including class or cluster creation or modification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation; Recording or statistical evaluation of user activity, e.g. usability assessment
    • 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
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass

Similar Documents

Publication Publication Date Title
Jiang et al. What causes my test alarm? Automatic cause analysis for test alarms in system and integration testing
Shokripour et al. Why so complicated? simple term filtering and weighting for location-based bug report assignment recommendation
Menzies et al. Problems with Precision: A Response to" comments on'data mining static code attributes to learn defect predictors'"
US20200380336A1 (en) Real-Time Predictive Maintenance of Hardware Components Using a Stacked Deep Learning Architecture on Time-Variant Parameters Combined with a Dense Neural Network Supplied with Exogeneous Static Outputs
JP7059220B2 (en) Machine learning program verification device and machine learning program verification method
Otoom et al. Severity prediction of software bugs
Chen et al. Software process evaluation: A machine learning approach
CN111651340B (en) Alarm data rule mining method and device and electronic equipment
US20240303100A1 (en) Systems and methods for automated process discovery
Navinchandran et al. Studies to predict maintenance time duration and important factors from maintenance workorder data
CN116882978B (en) Deep sea submersible operation and maintenance support system based on product information frame
Kern et al. Cross-disciplinary method for predicting and reducing human error probabilities in manual assembly operations
Haghighi et al. Applying mining schemes to software fault prediction: A proposed approach aimed at test cost reduction
Martins et al. Supervised learning for test suit selection in continuous integration
Kassner et al. Exploring Text Classification for Messy Data: An Industry Use Case for Domain-Specific Analytics.
John et al. Test case Recommendation for regression with Named Entity Recognition for test step prediction
KR101691083B1 (en) System and Method for Bug Fixing Developers Recommendation and Bug Severity Prediction based on Topic Model and Multi-Feature
Hübner et al. Using interaction data for continuous creation of trace links between source code and requirements in issue tracking systems
Zhang et al. How to recommend appropriate developers for bug fixing?
Schuh et al. Event log-based weaknesses detection in business processes
Dhanalaxmi et al. A fault prediction approach based on the probabilistic model for improvising software inspection
Mnkandla et al. Software defect prediction using process metrics elasticsearch engine case study
Zalmanovici et al. Cluster-based test suite functional analysis
Yang Multivariate statistical methods and Six-Sigma
Srinivasan et al. Modelling an optimized warranty analysis methodology for fleet industry using data mining clustering methodologies