Patent application EP18198499A (EP), filed 2018-10-03
Publication number: EP3467725A1
Title: CLASSIFYING WARNING MESSAGES GENERATED BY SOFTWARE DEVELOPER TOOLS
Abstract: A method for classifying warning messages generated by software developer tools includes receiving a first data set. The first data set includes a first plurality of data entries, where each data entry is associated with a warning message generated based on a first set of software codes, includes indications for a plurality of features, and is associated with one of a plurality of class labels. A second data set is generated by sampling the first data set. Based on the second data set, at least one feature is selected from the plurality of features. A third data set is generated by filtering the second data set with the selected at least one feature. A machine learning classifier is determined based on the third data set. The machine learning classifier is used to classify a second warning message generated based on a second set of software codes to one of the plurality of class labels.
IPC codes: G06F  11/    36            A I
G06F   8/    41            A I
G06N  20/    20            A I
G06N   5/    00            A I
Claims: number: 1
text: A method, comprising:
number: 2
text: The method of claim 1, wherein the plurality of class labels includes a first class label for fixing the warning message and a second class label for ignoring the warning message.
number: 3
text: The method of claim 1, wherein the first data set is an imbalanced data set.
number: 4
text: The method of claim 1, wherein the plurality of features includes features associated with at least one of a software development process, a programming code, a software code change, or a fault finding tool analysis.
number: 5
text: The method of claim 1, wherein sampling the first data set includes at least one of over-sampling a minority class or under-sampling a majority class.
number: 6
text: The method of claim 1, wherein determining the machine learning classifier includes dividing the third data set into a training data set and a testing data set, and wherein the training data set is used to determine the machine learning classifier, and the testing data set is used to evaluate a performance of the machine learning classifier, and optionally
wherein at least one of a stratified sampling or a stratified K-fold sampling is applied to the training data set, and/or optionally
wherein the first data set is sampled using a sampling method, and the method further comprises:
number: 7
text: The method of claim 1, wherein the machine learning classifier is a weighted random forest classifier.
number: 8
text: The method of claim 1, further comprising:
number: 9
text: The method of claim 1, wherein the at least one feature is selected based on a cut-off value.
number: 10
text: A device, comprising:
number: 11
text: The device of claim 10, wherein the first data set is an imbalanced data set.
number: 12
text: The device of claim 10, wherein sampling the first data set includes at least one of over-sampling a minority class or under-sampling a majority class.
number: 13
text: The device of claim 10, wherein determining the machine learning classifier includes dividing the third data set into a training data set and a testing data set, and wherein the training data set is used to determine the machine learning classifier, and the testing data set is used to evaluate a performance of the machine learning classifier.
number: 14
text: The device of claim 13, wherein the first data set is sampled using a sampling method, and the at least one hardware processor is further configured to:
number: 15
text: A computer-readable medium containing instructions which, when executed, cause a computing device to perform the method of any one of claims 1 to 9.
Office action: search_report, dated 2019-04-10
Rejection reasons: novelty_epc_54
Prior art cited: ref_id: US2017212829A1
ref_type: patent
severity: background
ref_id: - SARAH HECKMAN ET AL: "A systematic literature review of actionable alert identification techniques for automated static code analysis", INFORMATION AND SOFTWARE TECHNOLOGY, ELSEVIER, AMSTERDAM, NL, vol. 53, no. 4, 19 December 2010 (2010-12-19), pages 363 - 387, XP028145383, ISSN: 0950-5849, [retrieved on 20101224], DOI: 10.1016/J.INFSOF.2010.12.007
ref_type: npl
severity: background
ref_id: - QUINN HANAM ET AL: "Finding patterns in static analysis alerts: improving actionable alert ranking", MINING SOFTWARE REPOSITORIES, 1 January 2014 (2014-01-01), 2 Penn Plaza, Suite 701  New York  NY  10121-0701  USA, pages 152 - 161, XP055561712, ISBN: 978-1-4503-2863-0, DOI: 10.1145/2597073.2597100
ref_type: npl
severity: background
ref_id: US2017212829
ref_type: patent
severity: background
ref_id: - SARAH HECKMAN ET AL, "A systematic literature review of actionable alert identification techniques for automated static code analysis", INFORMATION AND SOFTWARE TECHNOLOGY, ELSEVIER, AMSTERDAM, NL, vol. 53, no. 4, doi:10.1016/J.INFSOF.2010.12.007, ISSN 0950-5849, (20101219), pages 363 - 387, (20101224), XP028145383 [X] 1-4,9-11,15 * abstract * * 1. Introduction * * 3. Detecting alert patterns * * 4. Method * * 5. Evaluation * * 6. Results * [I] 3,5-8,12-14
ref_type: npl
severity: novelty_destroying
ref_id: - QUINN HANAM ET AL, "Finding patterns in static analysis alerts: improving actionable alert ranking", MINING SOFTWARE REPOSITORIES, 2 Penn Plaza, Suite 701 New York NY 10121-0701 USA, (20140101), doi:10.1145/2597073.2597100, ISBN 978-1-4503-2863-0, pages 152 - 161, XP055561712 [A] 1-15 * abstract * * 1. Introduction * * 5. AAIT approaches and evaluation * * 6. Classification AAITs * * 7. Prioritizing results *
ref_type: npl
severity: background
Final disposition: pending (2019-04-10)