Data Mining and Machine Learning for Software Engineering

This paper solves the problem of modeling the scheme for developing software systems, which can be used in building solutions for secure energy networks. A development scheme is proposed in a set of representations through which each program of the software complex passes, namely the following representations: idea, conceptual model, architecture, algorithm, source code, graphic code, abstract syntax tree, assembler code, machine code, byte code, executed code. The main properties of each representation are indicated, such as the form (text, graphic, programming language, binary, and decoded), development (transformation) methods, as well as vulnerabilities that are detected in it. An example of each representation is given, particularly as applied to the energy networks. The scheme elements (representations, vulnerabilities, forms, etc.) and the main operations for working with their elements (representation transformation, vulnerability injection, and detection) are presented in an analytical form. An example of a development scheme for a simple software complex of energy networks is given. The classification of vulnerabilities is introduced; it divides the vulnerabilities according to the structural level, functioning disruption, and information impact. The vulnerabilities in each of the views are substantiated using the common vulnerabilities and exposures (CVE) database. An experiment was conducted to demonstrate the vulnerability spread across representations during the development of a software complex example for the energy network. The features of the applications of the obtained results for energy networks are taken into account. The advantages, disadvantages, and limitations of the study, as well as ways to eliminate them, are discussed.

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“…Kiyak [11] demonstrates the possibility of using various machine learning methods, such as clustering and regression, for SDLC modeling.…”

Section: Sdlc Modelsmentioning

confidence: 99%

“…The representation is the most "far" from the idea and close enough to the machine code, with the exception that it reflects the SW in a decoded form, directly executed on the CPU. We will write the representation as R 11 .…”

Section: Executed Code (No 11)mentioning

confidence: 99%

Modeling the Development of Energy Network Software, Taking into Account the Detection and Elimination of Vulnerabilities

et al. 2023

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“…Many of the metrics used to enhance quality attributes ( coupling, complexity, and cohesion) provide ideas for improving quality for developers. Kiyak [62] utilized data mining and refactoring even though refactoring automated not provide the required performance; the manual refactoring process takes a long time through algorithms for unsupervised learning that decrease the numeral of refactoring choices and had a positive impact on quality. Increases code readability and source code maintainability; reduces the complexity of the software system .…”

Section: Related Research To the Quality Of Codementioning

confidence: 99%

A literature review for measuring maintainability of code clone

Khaleel,

Al-Khatouni

2023

IJEECS

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Software organizations face constant pressure due to stakeholder requirements and the increasing complexity of software systems. This complexity, combined with defects in code quality and failures, can pose risks to software systems. To ensure code is understood before maintenance, developers must spend over 60% of their time modifying and improving code quality, which is costly. This study examines the impact of code refactoring activities on software maintainability and quality by reviewing relevant research and explaining key terms. The research finds that refactoring activities can enhance specific quality characteristics, including maintainability, understandability, and testability. The study also identifies important factors that should be considered when developing refactoring tools. Refactoring enables code improvement without altering program behavior and can be applied multiple times to source code.

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Practically Implementation of Information Loss: Sensitivity, Risk by Different Feature Selection Techniques

et al. 2022

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The modern perspective to deal with bulky data generations schemes of latest technologies in terms of dimensionality and sample size to extract meaningful information also to support automated knowledge discovery and pattern recognition process form datasets a lot of Data Mining (DM) and Machine Learning (ML) techniques developed. In each dataset features are the key factors for machine learning task. In modern research mindset classification algorithms are focused to get high accuracy by taking in account prior features and less focus on features having low characteristic values. In this paper we focused on those features which are usually ignored in selection phase as low scale features which may decrease model performance but in future for the most sensitive scenarios will focus on minor information which will alert about performance fluctuation in practical implementation of that model. For practical verification of our concept, we implemented rule-based classification algorithms and different features selection techniques with 3 search methods using WEKA data mining tool. The experimental results show that the fewer selected features provide high accuracy i.e > 90% in some cases having less focus on specificity.

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Data Mining and Machine Learning for Software Engineering

Cited by 4 publications

References 100 publications

Modeling the Development of Energy Network Software, Taking into Account the Detection and Elimination of Vulnerabilities

Modeling the Development of Energy Network Software, Taking into Account the Detection and Elimination of Vulnerabilities

A literature review for measuring maintainability of code clone

Practically Implementation of Information Loss: Sensitivity, Risk by Different Feature Selection Techniques

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