# US 10,372,593 B2 · System and method for resource modeling and simulation in test planning > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,372,593 B2 - Document ID: US10372593B2 - Status: Granted - Kind: B2 - Application number: 15/421,479 - Filed: 2017-02-01 - Published: 2017-05-18 - Granted: 2019-08-06 - Earliest claimed: 2009-09-11 - Assignees: International Business Machines Corporation - Applicants: International Business Machines Corporation - Inventors: Kathryn A. Bassin; Howard M. Hess; Sheng Huang; Steven M. Kagan; Shao C. Li; Zhong J. Li; He H. Liu; Susan E. Smith; Hua F. Tan; Li Wang; Jun Zhu - Web page: https://patentgrep.com/patent/US10372593B2 ## CPC - G06F 11/3684: {for test design, e.g. generating new test cases} - G06F 8/70: Software maintenance or management - G06F 11/3672: {Test management} - G06Q 10/06: Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling - G06Q 10/06312: {Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling} - G06Q 10/06313: {Resource planning in a project environment} - G06Q 10/067: Enterprise or organisation modelling - G06Q 10/103: {Workflow collaboration or project management} - G06Q 30/0249: {based upon budgets or funds} - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR ## Abstract A method includes generating a test model based on at least one of test group dependencies and test group constraints and generating a resource base. The method includes generating a cost model and generating a resource allocation plan based on the test model, the resource base, and the cost model. ## Claims ### Claim 1 A method comprising: identifying constraints including at least one of a test environment, language and knowledge for a test for test cases; generating a test model from the test cases and test case dependencies in a test modeling module of a test planning optimization workbench which includes a processor and a memory device; determining a test estimate effort for each test case by leveraging knowledge of at least one of best practices and an applicable reference project using pre-defined rules, logic or probability tables, wherein the pre-defined rules, logic and probability tables are based on analysis and data-mining of historic data from past projects; generating a cost model in a cost module based on at least one constraint; and generating a resource plan in a resource modeling and simulation module based on a resource base, the test model, and the cost model, wherein the resource plan allocates resources defined in the resource base to the test cases, minimizes a total cost base on the cost model, conforms to the test case dependencies defined in the test model, and satisfies the at least one constraint defined in the cost model; wherein the test model comprises a dependency graph and a critical path. ### Claim 2 (depends on 1) The method of claim 1, further comprising dynamically updating the resource plan in response to an adjusting of the at least one constraint. ### Claim 3 (depends on 1) The method of claim 1, further comprising generating an alert when a project delay exceeds a delay warning threshold. ### Claim 4 (depends on 1) The method of claim 1, further comprising displaying the generated resource plan. ### Claim 5 (depends on 1) The method of claim 1, further comprising receiving data for defining and constructing the cost model from a user. ### Claim 6 (depends on 5) The method of claim 5, wherein the received data for the defining and the constructing the cost model includes at least one of: one or more warning thresholds, expected escape defects, one or more business cost configurations, one or more project delay cost configurations, and one or more defect fix cost configurations. ### Claim 7 (depends on 6) The method of claim 6, further comprising sending a warning to a user when a project schedule exceeds the one or more warning thresholds. ### Claim 8 (depends on 7) The method of claim 7, wherein the warning is one of an email and a pop-up message prompting the user to take action to address the exceeded project schedule. ### Claim 9 (depends on 8) The method of claim 8, further comprising receiving a resource adjustment from the user and generating a new resource plan based on the received resource adjustment. ### Claim 10 (depends on 1) The method of claim 1, wherein the resource plan includes a micro plan which can be displayed with a simulator. ### Claim 11 (depends on 10) The method of claim 10, further comprising identifying triggers which each test case is associated with, and associating the triggers with any test case dependencies related to each test case. ### Claim 12 (depends on 10) The method of claim 10, further comprising enhancing the test case dependencies with the triggers. ### Claim 13 (depends on 10) The method of claim 10, wherein a relationship between the test cases and the triggers is defined in the micro plan. ### Claim 14 (depends on 1) The method of claim 1, wherein the analysis includes Orthogonal Defect Classification/Defect Reduction Method defect analysis. ### Claim 15 (depends on 14) The method of claim 14, further comprising storing the test estimate effort determined for each test case in the memory in the test planning optimization workbench. ### Claim 16 A system comprising: a test planning optimization workbench including: a processor; a memory; a test modeling module operable to identify constraints including at least one of a test environment, language and knowledge for a test for test cases, generate a test model from the test cases and test case dependencies, and determine a test estimate effort for each test case by leveraging knowledge of at least one of best practices and an applicable reference project using pre-defined rules, logic or probability tables, wherein the pre-defined rules, logic and probability tables are based on analysis and data-mining of historic data from past projects; a cost module operable to generate a cost model based on at least one constraint; and a resource modeling and simulation module operable to generate a resource plan based on a resource base, the test model, and the cost model, wherein the resource plan allocates resources defined in the resource base to the test cases, minimizes a total cost base on the cost model, conforms to the test case dependencies defined in the test model, and satisfies the at least one constraint defined in the cost model; wherein the test model comprises a dependency graph and a critical path. ### Claim 17 (depends on 16) The system of claim 16, wherein the test planning optimization workbench dynamically updates the resource plan in response to an adjusting of the at least one constraint. ### Claim 18 (depends on 16) The system of claim 16, wherein the test planning optimization workbench generates an alert when a project delay exceeds a delay warning threshold. ### Claim 19 A computer program product comprising a computer readable storage memory having readable program code embodied in the storage memory, the program code when executed on a computing device causes the computing device to: identify constraints including at least one of a test environment, language and knowledge for a test for test cases; generate a test model from the test cases and test case dependencies in a test modeling module of a test planning optimization workbench which includes a processor and a memory device; determine a test estimate effort for each test case by leveraging knowledge of at least one of best practices and an applicable reference project using pre-defined rules, logic or probability tables, wherein the pre-defined rules, logic and probability tables are based on analysis and data-mining of historic data from past projects; generate a cost model in a cost module based on at least one constraint; and generate a resource plan in a resource modeling and simulation module based on a resource base, the test model, and the cost model, wherein the resource plan allocates resources defined in the resource base to the test cases, minimizes a total cost base on the cost model, conforms to the test case dependencies defined in the test model, and satisfies the at least one constraint defined in the cost model; wherein the test model comprises a dependency graph and a critical path. ### Claim 20 (depends on 19) The computer program product of claim 19, wherein the program code further causes the computing device to dynamically update the resource plan in response to a change of the at least one constraint.