Error-Type—A Novel Set of Software Metrics for Software Fault Prediction

Phung, Khoa; Ogunshile, Emmanuel; Aydın, Mehmet Emin

doi:10.1109/access.2023.3262411

Cited by 4 publications

(3 citation statements)

References 58 publications

(93 reference statements)

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“…This form of network integrates developer, module dependency, and contribution of developer in order to investigated their joint influence towards software quality. The work carried out by Phung et al [59] have presented a unique scheme where software metric is formulated for error representation associated with java-based runtime. A formal modelling method is used along with machine learning in order to evaluate the patterns.…”

Section: Other Approachesmentioning

confidence: 99%

Insights of effectivity analysis of learning-based approaches towards software defect prediction

Rai,

Arcot Prashant

2024

IJECE

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Software defect prediction is one of the essential sets of operation towards mitigating issues of risk management in software development known to contribute towards enhancing the quality of software. There is evolution of various methodologies towards resolving this issue while learning-based methodology is witnessed to be the most dominant contributor. The problem identified is that there are yet many unsolved queries associated with practical viability of such learning-based approach adoption in software quality management. Proposed approaches discussed in this paper contributes towards mitigating this challenge by introducing a simplified, compact, and crisp analysis of effectiveness associated with learning-based schemes. The paper presents its major findings of effectivity analysis of machine learning, deep learning, hybrid, and other miscellaneous approaches deployed for fault prediction followed by highlighting research trend. The major findings infer that feature selection, data imbalance, interpretability, and in adequate involvement of context are prime gaps in existing methods. The paper also contributes towards research gap as well as essential learning outcomes of present review work.

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Section: Other Approachesmentioning

confidence: 99%

Insights of effectivity analysis of learning-based approaches towards software defect prediction

Rai,

Arcot Prashant

2024

IJECE

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“…The "Data Flow" test cases are those that have a flow variation of "31" or above, which include a variety of data flow constructions. 3 In this research, we have used the terms "rule" and "checker" interchangeably, referring to them by their respective terms in the documentation of each tool.…”

Section: B: Sonarqube Community Edition (Sonar)mentioning

confidence: 99%

“…The presence of programming errors reduces the quality of the software and increases the cost of development life cycle [3]. Poor program quality is a leading cause of security vulnerabilities.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Evaluation of Static Analysis Tools for Their Performance in Finding Vulnerabilities in Java Code

Alqaradaghi,

Kozsik

2024

IEEE Access

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Various static code analysis tools have been designed with the aim of detecting software faults and security vulnerabilities automatically. This paper aims to 1) Conduct an empirical evaluation to assess the performance of five free and state-of-the-art static analysis tools in detecting Java security vulnerabilities using a well-defined and repeatable approach. 2) Report on the vulnerabilities that are best and worst detected by static Java analyzers. We used the Juliet benchmark test suite in a controlled experiment to assess the effectiveness of five widely used Java static analysis tools. The vulnerabilities were successfully detected by one, two, or three tools. Only one vulnerability has been detected by four tools. The tools missed 13% of the Java vulnerability categories appearing in our experiment. More critically, none of the five tools could identify all the vulnerabilities in our experiment. We conclude that, despite recent improvements in their methodologies, current state-of-the-art static analysis tools are still ineffective for identifying the security vulnerabilities occurring in a small-scale, artificial test suite.

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HURON: A Quantitative Framework for Assessing Human Readability in Ontologies

Abad-Navarro,

Martínez-Costa,

Fernández-Breis

2023

IEEE Access

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The increasing use of ontologies requires their quality assurance. Ontology quality assurance consists of a set of activities that allow analyzing the ontology, identifying strengths and weaknesses, and proposing improvement actions. Human readability is a quality aspect that improves the use and reuse of ontologies. The human readable content refers to the natural language content consumed by humans and by the growing number of embedding methods applied to ontologies. The ontology community has proposed best practices related to human readability, but there is no standardized framework for its assessment. We aim to provide a framework for analyzing the human readability based on quantitative metrics for supporting ontology developers' decisions. We present the HURON framework, which consists of the specification of five quantitative metrics related to the human readability of ontology content, and a software tool to implement them. The metrics take into account the number of names, descriptions, or synonyms, and also assess the application of systematic naming conventions and the 'lexically suggest, lexically define' principle. Target values are provided for each metric to help to interpret the values. HURON can also be used to assess compliance with best practices. We have applied HURON to a representative set of biomedical ontologies, the OBO Foundry repository. The results showed that, in general, the OBO Foundry ontologies comply with the expected number of descriptions and names in their classes, and both lexical and semantically formalized contents are aligned. However, most of the ontologies did not follow a systematic naming convention. In general, the ontologies of this repository show adherence to some of the best practices, although areas of improvement were identified. A number of recommendations are made for ontology developers and users. INDEX TERMS knowledge engineering, ontologies, quality assurance, readability metrics, semantic web I. INTRODUCTIONOntologies play a key role in knowledge engineering by providing a common conceptualization of a domain. Ontologies have been successfully applied in different domains, but especially in biology and biomedicine, with different purposes [1][2][3][4][5].At the time of writing, repositories like BioPortal [6] had more than 1,000 ontologies and both the Open Biological and Biomedical Ontology (OBO) Foundry [7] and the Ontology Lookup Service (OLS) [8] had more than 250 ontologies. The number of ontologies in these repositories is continuously increasing, which demonstrates their relevance and impact.In contrast to other artifacts used in data management systems, such as relational databases, developed specifically for particular applications, ontologies should be created in a standardized way to facilitate their reuse. The sharing and reuse orientation of ontologies have also made them fundamental for obtaining Findable, Accessible, Interoperable, and Reusable (FAIR) datasets [9]. As a consequence, assuring the quality of ontologies has become a major need.

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Error-Type—A Novel Set of Software Metrics for Software Fault Prediction

Cited by 4 publications

References 58 publications

Insights of effectivity analysis of learning-based approaches towards software defect prediction

Insights of effectivity analysis of learning-based approaches towards software defect prediction

Comprehensive Evaluation of Static Analysis Tools for Their Performance in Finding Vulnerabilities in Java Code

HURON: A Quantitative Framework for Assessing Human Readability in Ontologies

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