PATENT CLAIM ANALYSIS

Application Number: 16013848
Application Type: Utility
Filing Date: 2018-06
Publication Date: 2019-12
Patent Classification: ["717", "128000"]

Abstract:
Disclosed is a system for removing bugs present in a software code. A determination module determines a usage pattern of a software code by using an Artificial Neural Network (ANN) technique. A comparison module compares the usage pattern with a set of pre-stored usage patterns of software applications similar to the software code. An execution module executes a set of test suites, on the software code, associated to at least one software application of the software applications, when a usage pattern of the at least one software application is matched with the usage pattern of the software code. An identification module identifies a code snippet comprising the bug. A recommendation module recommends a code patch, corresponding to the code snippet, from a ranked list of code patches determined by a Deep RNN technique. Further, a replacement module replaces the code patch with the code snippet thereby removing the bug.

Claim (Index 7):
A system for removing a bug present in a software code, the system comprising:\n a processor; and a memory coupled to the processor, wherein the processor is capable of executing a plurality of modules stored in the memory, and wherein the plurality of modules comprising:\n a determination module for determining a usage pattern of a software code by using an Artificial Neural Network (ANN) technique; \n a comparison module for comparing the usage pattern with a set of pre-stored usage patterns of software applications, similar to the software code, wherein the usage pattern is compared by using a Deep Convolution Neural Network (Deep CNN) based transfer learning; \n an execution module for executing a set of test suites, on the software code, associated to at least one software application of the software applications, when a usage pattern of the at least one software application is matched with the usage pattern of the software code; \n an identification module for identifying a code snippet comprising a bug in the software code by,\n localizing the bug present in a line of code, of the software code, based on a stack trace, wherein the bug is localized upon determination of an occurrence of the bug in the software code, and wherein the occurrence of the bug is determined based on execution of the set of test suites, and \n determining the code snippet corresponding to the line of code by using a Deep Recurrent Neural Network (Deep RNN) technique; \n \n a recommendation module for recommending a code patch, corresponding to the code snippet, from a ranked list of code patches determined by the Deep RNN technique; and \n a replacement module for replacing the code patch with the code snippet thereby removing the bug present in the code.

Metadata:
- Claim Count in Document: 29.0
- Percentile: 94.0
- Lexical Diversity: 2.24658
- Patent Class: 717.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15703879', '15065237', '15703896', '15703885', '15821231']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3326080132198384
- 35 USC 102 Novelty (BERT): 0.5054415879781712
- Combined Prediction Score: 0.3498913706956716
- Mean Citation Score: 181.513088
- Max Citation Score: 198.67316
- Similarity Product: 138.97757617801665

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 0
- Combined Label: 1
- Label 101 Adjusted: 1

Dataset: test