1. A system for advance feature selection method for software defect prediction comprising of Better and updated wrapper method.

2. Relevance based dynamic re-ranking of features

3. More improved Software defect prediction

Field of the Invention

[0001] The invention is related to Computer Science- Software engineering with the outcome of estimation of error prone area.

Background

[0002] Dimensionality reduction
[0003] Reduction of computing required
[0004] Improved turnaround time
[0005] Better utilization of resources
[0006] More weightage to relevant features
[0007] Legacy of the datasets of projects
[0008] More accurate assumptions
[0009] Fewer area of concerns after every build
[00010] Latest inclusion of resolutions
[00011] Successful updates
[00012] All publications herein are incorporated by reference to the same extent as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the
definition of that term provided herein applies and the definition of that term in the reference does not apply.
[00013] In some embodiments, the numbers expressing quantities of ingredients, properties such as concentration, reaction conditions, and so forth, used to describe and claim certain embodiments of the invention are to be understood as being modified in some instances by the term “about.” Accordingly, in some embodiments, the numerical parameters set forth in the written description and attached claims are approximations that can vary depending upon the desired properties sought to be obtained by a particular embodiment. In some embodiments, the numerical parameters should be construed in light of the number of reported significant digits and by applying ordinary rounding techniques. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of some embodiments of the invention are approximations, the numerical values set forth in the specific examples are reported as precisely as practicable. The numerical values presented in some embodiments of the invention may contain certain errors necessarily resulting from the standard deviation found in their respective testing measurements.
[00014] As used in the description herein and throughout the claims that follow, the meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.
[00015] The recitation of ranges of values herein is merely intended to serve as a shorthand method of referring individually to each separate value falling within the range. Unless otherwise indicated herein, each individual value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g. “such as”) provided with respect to certain embodiments herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non- claimed element essential to the practice of the invention.
[00016] Groupings of alternative elements or embodiments of the invention disclosed herein are not to be construed as limitations. Each group member can be referred to and claimed individually or in any combination with other members of the group or other elements found herein. One or more members of a group can be included in, or deleted from, a group for reasons of convenience and/or patentability. When any such inclusion or deletion occurs, the specification is herein deemed to contain the group as modified thus fulfilling the written description of all Markush groups used in the appended claims.

Objects of the Invention

[00017] An object of the present disclosure is to overcome one or more drawbacks associated with conventional mechanisms.
[00018] An object of the present invention is to improve the defect prediction using better feature selection.
[00019] An object of the present invention is to improve better wrapper for feature set.
[00020] An object of the present invention is to provide more efficient usage of resources.
[00021] An object of the present disclosure is also helps in informing relevant end users.
[00022] An object of the present to provide a smart and faster way to identify error prone areas.

Summary

[00023] The software development lifecycle (SDLC) is a formal framework that was created with the goal of producing or developing high-quality software and systems
[00024] The SDLC combines progressive changes to create a timely and dependable software system procedures such as eliciting requirements, reviewing software systems, and designing software systems and software system maintenance, which must be followed and implemented with care
[00025] However, because the SDLC's step-by-step processes are carried out by humans, faults are inevitable and are unavoidable. These problems are prevalent and reoccurring today due to the vast scale and interdependence in modules or components of software systems. These mistakes, if not remedied soon, will lead to unstable computing architectures and, eventually, software failure.
[00026] As a result of flaws in information system modules or components, software systems will be faulty and of low quality. In addition, software system vulnerabilities might upset end-users and customers when the failing software system fails to perform as expected after wasting valuable resources (time and effort)

Drawings

Fig-1

Fig-2

Brief Description of the Drawings

[00027] FIG. 1 illustrates an exemplary architecture of a system for the wrapper-based feature selection and defect prediction
[00028] FIG. 2 illustrates exemplary functional instructions for feature selection, in accordance with embodiments of the present disclosure.

Detailed Description

[00029] The following discussion provides many example embodiments of the inventive subject matter. Although each embodiment represents a single combination of inventive elements, the inventive subject matter is considered to include all possible combinations of the disclosed elements. Thus, if one embodiment comprises elements A, B, and C, and a second embodiment comprises elements B and D, then the inventive subject matter is also considered to include other remaining combinations of A, B, C, or D, even if not explicitly disclosed.
[00030] The present invention relates to a system and method for feature selection. More particularly, to a system and method for software defect prediction.
[00031] FIG. 2 illustrates an exemplary architecture of a system 200 for software prediction, in accordance with an embodiment of the present disclosure.
[00032] Throughout the present disclosure, the term ‘processing means’ or ‘microprocessor’ or ‘processor’ or ‘processors’ includes, but is not limited to, a microprocessor, a microcontroller, a complex instruction set computing (CISC) microprocessor, a reduced instruction set (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, or any other type of processing circuit.
[00033] In an aspect, any or a combination of machine learning mechanisms such as decision tree learning, Bayesian network, deep learning, random forest, supervised vector machines, reinforcement learning, prediction models, Statistical Algorithms, Classification, Logistic Regression, Support Vector Machines, Linear Discriminant Analysis, K- Nearest Neighbours, Decision Trees, Random Forests, Regression, Linear Regression, Support Vector Regression, Logistic Regression, Ridge Regression, Partial Least-Squares Regression, Non-Linear Regression, Clustering, Hierarchical Clustering – Agglomerative, Hierarchical Clustering
– Divisive, K-Means Clustering, K-Nearest Neighbours Clustering, EM (Expectation Maximization) Clustering, Principal Components Analysis Clustering (PCA), Dimensionality Reduction, Non-Negative Matrix Factorization (NMF), Kernel PCA, Linear Discriminant Analysis (LDA), Generalized Discriminant Analysis (kernel trick again), Ensemble Algorithms, Deep Learning, Reinforcement Learning, AutoML (Bonus) and the like can be employed to learn sensor/hardware components.
[00034] The term “non-transitory storage device” or “storage” or “memory,” as used herein relates to a random-access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
[00035] It should be apparent to those skilled in the art that many more modifications besides those already described are possible without departing from the inventive concepts herein. The inventive subject matter, therefore, is not to be restricted except in the spirit of the appended claims. Moreover, in interpreting both the specification and the claims, all terms should be interpreted in the broadest possible manner consistent with the context. In particular, the terms “comprise” and “comprising” should be interpreted as referring to elements, components, or steps in a non-
exclusive manner, indicating that the referenced elements, components, or
steps may be present, or utilized, or combined with other elements, components, or steps that are not expressly referenced. Where the specification claims refer to at least one of something selected from the group consisting of A, B, C …. and N, the text should be interpreted as requiring only one element from the group, not A plus N, or B plus N, etc.

Advantages of the Invention

[00036] Knowing faults prone module
[00037] Improved Dimensionality reduction
[00038] Less computing capacity required
[00039] Better computation time
[00040] Efficient and optimized utilization of resources.
[00041] Knowing feature set better

We Claim:

1. A system for advance feature selection method for software defect prediction comprising of Better and updated wrapper method.
2. Relevance based dynamic re-ranking of features

3. More improved Software defect prediction