Evaluating micro patterns and software metrics in vulnerability prediction

Locating vulnerable lines of code in large software systems needs huge efforts from human experts. This explains the high costs in terms of budget and time needed to correct vulnerabilities. To minimize these costs, automatic solutions of vulnerabilities prediction have been proposed. Existing machine learning (ML)-based solutions face difficulties in predicting vulnerabilities in coarse granularity and in defining suitable code features that limit their effectiveness. To addressee these limitations, in the present work, the authors propose an improved ML-based approach using slice-based code representation and the technique of TF-IDF to automatically extract effective features. The obtained results showed that combining these two techniques with ML techniques allows building effective vulnerability prediction models (VPMs) that locate vulnerabilities in a finer granularity and with excellent performances (high precision (>98%), low FNR (<2%) and low FPR (<3%) which outperforms software metrics and are equivalent to the best performing recent deep learning-based approaches.

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Fabrication of 3D Micro Patterns on Concave Substrate and Its Application for the Fabrication of Cylindrical Polymer Stamps

IEEJ Transactions on Sensors and Micromachines ◽

10.1541/ieejsmas.132.303 ◽

2012 ◽

Vol 132 (9) ◽

pp. 303-308

Author(s):

Jongho Park ◽

Nobuyuki Takama ◽

Hiroyuki Fujita ◽

Beomjoon Kim

Keyword(s):

Micro Patterns

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Neural Network Model to Calculate the Creep Data Using Size

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i8.60 ◽

2017 ◽

Vol 7 (8) ◽

pp. 257

Author(s):

Seetharam .K ◽

Sharana Basava Gowda ◽

. Varadaraj

Keyword(s):

Neural Network ◽

Risk Factors ◽

Software Engineering ◽

Network Model ◽

Neural Network Model ◽

Software Metrics ◽

Creep Data ◽

Engineering Software ◽

Function Points ◽

Different Levels

In Software engineering software metrics play wide and deeper scope. Many projects fail because of risks in software engineering development[1]t. Among various risk factors creeping is also one factor. The paper discusses approximate volume of creeping requirements that occur after the completion of the nominal requirements phase. This is using software size measured in function points at four different levels. The major risk factors are depending both directly and indirectly associated with software size of development. Hence It is possible to predict risk due to creeping cause using size.

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A Literature Review Study of Software Defect Prediction using Machine Learning Techniques

International Journal of Emerging Research in Management and Technology ◽

10.23956/ijermt.v6i6.286 ◽

2018 ◽

Vol 6 (6) ◽

pp. 300 ◽

Cited By ~ 3

Author(s):

Feidu Akmel ◽

Ermiyas Birihanu ◽

Bahir Siraj

Keyword(s):

Machine Learning ◽

Software Metrics ◽

Quality Standard ◽

Machine Learning Techniques ◽

Software Systems ◽

Health Care Insurance ◽

Software Defect ◽

Learning Techniques ◽

Software Product

Software systems are any software product or applications that support business domains such as Manufacturing,Aviation, Health care, insurance and so on.Software quality is a means of measuring how software is designed and how well the software conforms to that design. Some of the variables that we are looking for software quality are Correctness, Product quality, Scalability, Completeness and Absence of bugs, However the quality standard that was used from one organization is different from other for this reason it is better to apply the software metrics to measure the quality of software. Attributes that we gathered from source code through software metrics can be an input for software defect predictor. Software defect are an error that are introduced by software developer and stakeholders. Finally, in this study we discovered the application of machine learning on software defect that we gathered from the previous research works.

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