DETAILED ACTION
This office action is responsive to claims 1 – 20 filed in this application Sethia et al., U.S. Patent Application No. 18/630,434 (Filed April 9, 2024) (“Sethia”).
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement(s) (IDS) filed on 4/9/2024 is compliance with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609. The references listed therein have been considered, and placed in the application file.
Claim Rejections 35 U.S.C. §103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 3 – 8, 10 – 15, and 17 - 20 are rejected under 35 U.S.C. 103 as being unpatentable over Warfield, United States Patent No. 5,754,760 (Patented May 19, 1998, filed May 30, 1996) (“Warfield”) in view of Kommineni et al., United States Patent Application Publication No. 2015/0261657 (Published September 17, 2015, filed February 12, 2015) (“Kommineni”).
Claims 1, 8, and 15
With respect to claims 1, 8, and 15, Warfield teaches the invention as claimed including a system, comprising:
a memory configured to store a total population of test cases, a selected population of test cases, a first set of fitness criteria, and a second set of fitness criteria, wherein the selected population of test cases is configured to test a functionality of one or more of a first instance of a software application or a second instance of the software application, and wherein the selected population of test cases comprises a subset of the total population of test cases; and one or more processors operably coupled to the memory and configured to: access the selected population of test cases and the first set of fitness criteria; determine, based on the selected population of test cases and the first set of fitness criteria, a fitness score for each test case of the selected population of test cases; iteratively execute a natural computing algorithm to identify one or more best possible test cases based at least in part on the fitness score for each test case of the selected population of test cases;… and execute, based at least in part on the output, the identified one or more best possible test cases to test the functionality of the one or more of the first instance of the software application or the second instance of the software application. {Test cases for a software application are provided to a genetic algorithm which iteratively (repeatedly) creates new generations of tests cases by selecting a subset of each previous generation based on a plurality of different types of fitness score data stored for each test, uses crossover points between prior tests and/or mutates values for the prior tests to generate the next generation of tests until a generation of test cases produces the “best” test cases based on the fitness criteria at which point the application may be tested using such scripts that were generated by the genetic algorithm. Warfield at Abstract; id. at col. 3 ll. 44 – 65, id. at col. 4 ll. 5 – 35; id. at col. 5 ll. 15 – 18; id. at col. 8 ll. 13 – 62; id. at col.10 ll. 16 – 38; id. at Claim 32 & fig. 1 (program storage device of computer 10 includes memory 12 and mass storage 19 for generating and storing the plurality of test scripts and fitness scores).}
However, Warfield doesn’t explicitly teach the limitation:
in response to determining that the identified one or more best possible test cases satisfies the second set of fitness criteria, generate an output comprising the identified one or more best possible test cases; {Kommineni does teach this limitation. Kommineni teaches that using a genetic algorithm to select the best test cases for testing an application, as taught in Warfield, may include selecting test cases for a software application that satisfy fitness criteria such as by using a genetic algorithm to select a specified minimum number of test cases that are required to achieve a specified level of branch coverage at which point the genetic algorithm is stopped and the application is tested using the selected test cases. Kommineni at Abstract; id. at ¶¶ 0027, 0047, 0048, and 0085; id. at fig. 2.
Warfield and Kommineni are analogous art because they are from the “same field of endeavor” and are both from the same “problem-solving area.” Specifically, they are both from the field of software testing, and both are trying to solve the problem of how to use a genetic algorithm to select test cases.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine using a genetic algorithm to select the best test cases for testing an application, as taught in Warfield, with using a genetic algorithm to select the best test cases based on selection criteria, as taught in Kommineni. Kommineni teaches that it is desirable to try and obtain a minimized number of test cases. Kommineni at ¶ 0002. Therefore, one having ordinary skill in the art would have been motivated to combine using a genetic algorithm to select the best test cases for testing an application, as taught in Warfield, with using a genetic algorithm to select the best test cases based on selection criteria, as taught in Kommineni, for the purpose of using criteria to select the best test cases with a method that requires selecting the best test cases.}
Claims 3, 10, and 17
With respect to claims 3, 10, and 17, Warfield and Kommineni teach the invention as claimed including:
wherein the one or more processors are further configured to: prior to determining the fitness score for each test case of the selected population of test cases: initialize the selected population of test cases; and assign each test case of the selected population of test cases to one of a plurality of test suites based at least in part on a functionality to be tested.
{Initial test cases for a software application are provided to a genetic algorithm which iteratively (repeatedly) creates new generations of tests cases by selecting a subset of each previous generation based on a plurality of different types of fitness score data stored for each test, uses crossover points between prior tests and/or mutates values for the prior tests to generate the next generation of tests until a generation of test cases produces the “best” test cases based on the fitness criteria at which point the application may be tested using such scripts that were generated by the genetic algorithm. Warfield at Abstract; id. at col. 3 ll. 44 – 65, id. at col. 4 ll. 5 – 35; id. at col. 5 ll. 15 – 18; id. at col. 8 ll. 13 – 62; id. at col.10 ll. 16 – 38; id. at Claim 32 & fig. 1 (program storage device of computer 10 includes memory 12 and mass storage 19 for generating and storing the plurality of test scripts and fitness scores).}
Claims 4, 11, and 18
With respect to claims 4, 11, and 18, Warfield and Kommineni teach the invention as claimed including:
wherein the natural computing algorithm comprises a genetic algorithm (GA). {Initial test cases for a software application are provided to a genetic algorithm which iteratively (repeatedly) creates new generations of tests cases by selecting a subset of each previous generation based on a plurality of different types of fitness score data stored for each test, uses crossover points between prior tests and/or mutates values for the prior tests to generate the next generation of tests until a generation of test cases produces the “best” test cases based on the fitness criteria at which point the application may be tested using such scripts that were generated by the genetic algorithm. Warfield at Abstract; id. at col. 3 ll. 44 – 65, id. at col. 4 ll. 5 – 35; id. at col. 5 ll. 15 – 18; id. at col. 8 ll. 13 – 62; id. at col.10 ll. 16 – 38; id. at Claim 32 & fig. 1 (program storage device of computer 10 includes memory 12 and mass storage 19 for generating and storing the plurality of test scripts and fitness scores).}
Claims 5, 12, and 19
With respect to claims 5, 12, and 19, Warfield and Kommineni teach the invention as claimed including:
wherein the one or more processors are further configured to execute the genetic algorithm (GA) by: selecting a set of test cases from the selected population of test cases, wherein the selected set of test cases comprises test cases having a highest fitness score; generating, based at least in part on the selected set of test cases, at least one new test case by 1) identifying a crossover point between at least one pair of test cases of the selected set of test cases and 2) alternating one or more values of a sequence of values representative of each test case of the at least one pair of test cases, the one or more values of the sequence of values being alternated until the identified crossover point between the at least one pair of test cases is reached; performing a mutation of the at least one new test case by altering one or more values of a sequence of values representative of the at least one new test case; and generating a new population of test cases, wherein the new population of test cases comprises the at least one new test case. {Initial test cases for a software application are provided to a genetic algorithm which iteratively (repeatedly) creates new generations of tests cases by selecting a subset of each previous generation based on a plurality of different types of fitness score data stored for each test, uses crossover points between prior tests and/or mutates values for the prior tests to generate the next generation of tests until a generation of test cases produces the “best” test cases based on the fitness criteria at which point the application may be tested using such scripts that were generated by the genetic algorithm. Warfield at Abstract; id. at col. 3 ll. 44 – 65, id. at col. 4 ll. 5 – 35; id. at col. 5 ll. 15 – 18; id. at col. 8 ll. 13 – 62; id. at col.10 ll. 16 – 38; id. at Claim 32 & fig. 1 (program storage device of computer 10 includes memory 12 and mass storage 19 for generating and storing the plurality of test scripts and fitness scores).}
Claims 6, 13, and 20
With respect to claims 6, 13, and 20, Warfield and Kommineni teach the invention as claimed including:
wherein the one or more processors are further configured to determine whether the identified one or more best possible test cases satisfies the second set of fitness criteria based at least in part on whether the identified one or more best possible test cases satisfies one or more of a credibility criterion, a feasibility criterion, a requirements coverage criterion, or a requirements traceability criterion. {Test cases are selected for a software application that satisfy fitness criteria such as by using a genetic algorithm to select a specified secondary criterion such as a minimum number of test cases that are required to achieve a specified level of branch coverage at which point the genetic algorithm is stopped and the application is tested using the selected test cases. Kommineni at Abstract; id. at ¶¶ 0027, 0047, 0048, and 0085; id. at fig. 2.}
Claims 7 and 14
With respect to claims 7 and 14, Warfield and Kommineni teach the invention as claimed including:
wherein the one or more processors are further configured to: prior to accessing the selected population of test cases and the first set of fitness criteria, generate the selected population of test cases based on the total population of test cases. {Initial test cases for a software application are generated from a total potential population and are provided to a genetic algorithm which iteratively (repeatedly) creates new generations of tests cases by selecting a subset of each previous generation based on a plurality of different types of fitness score data stored for each test, uses crossover points between prior tests and/or mutates values for the prior tests to generate the next generation of tests until a generation of test cases produces the “best” test cases based on the fitness criteria at which point the application may be tested using such scripts that were generated by the genetic algorithm. Warfield at Abstract; id. at col. 3 ll. 44 – 65, id. at col. 4 ll. 5 – 35; id. at col. 5 ll. 15 – 18; id. at col. 8 ll. 13 – 62; id. at col.10 ll. 16 – 38; id. at Claim 32 & fig. 1 (program storage device of computer 10 includes memory 12 and mass storage 19 for generating and storing the plurality of test scripts and fitness scores); id. at col. 7 ll. 43 – 45 & 60 – 62, id. at col. 8 ll. 8 – 12, and id. at col. 10 ll. 6 – 13 (initial test case generation and selection).}
Claims 2, 9, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Warfield and Kommineni in view of Mitchell et al., United States Patent Application Publication No. 2019/0171552 (Published June 6, 2019, filed December 1, 2017) (“Mitchell”).
Claims 2, 9, and 16
With respect to claims 2, 9, and 16, Warfield and Kommineni teach the invention as claimed, however, Warfield and Kommineni doesn’t explicitly teach the limitation:
wherein the first instance of the software application comprises an instance of the software application during development time, and wherein the second instance of the software application comprises an instance of the software application during runtime. {Mitchell does teach this limitation. Mitchell teaches that using a genetic algorithm to select the best test cases for testing an application, as taught in Warfield and Kommineni, may include performing the testing during both development and production stages. Mitchell at Abstract; id. at ¶¶ 0003, 0009, 0021 – 0025 (development and production testing); id. at ¶¶ 0007, 0026, 0027 (genetic algorithm).
Warfield, Kommineni, and Mitchell are analogous art because they are from the “same field of endeavor” and are both from the same “problem-solving area.” Specifically, they are both from the field of software testing, and both are trying to solve the problem of using a genetic algorithm to select test cases.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine using a genetic algorithm to select the best test cases for testing an application, as taught in Warfield and Kommineni, with performing the testing during both development and production stages, as taught in Mitchell. Kommineni teaches that it is desirable to try and obtain a minimized number of test cases. Kommineni at ¶ 0002. Therefore, one having ordinary skill in the art would have been motivated to combine using a genetic algorithm to select the best test cases for testing an application, as taught in Warfield and Kommineni, with performing the testing during both development and production stages, as taught in Mitchell, for the purpose of using the genetic algorithm to select the best test cases for testing at multiple different testing stages.}
Conclusion
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//T.H./ April 1, 2026
Examiner, Art Unit 2199
/LEWIS A BULLOCK JR/Supervisory Patent Examiner, Art Unit 2199
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