Automatic Calculation of Process Metrics and their Bug Prediction Capabilities

Gyimesi, Péter

doi:10.14232/actacyb.23.2.2017.7

Cited by 3 publications

(6 citation statements)

References 25 publications

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“…In our previous papers [3,4], we published a graph schema for calculating software process metrics. Now we will briefly describe it together with the minor modifications that we made to calculate bug numbers.…”

Section: Methodsmentioning

confidence: 99%

“…The definitions of additional graph parts which we did not use in this study can be found in our previous paper [4]. We will now proceed to the bug number calculation.…”

Section: Methodsmentioning

confidence: 99%

“…In these two steps, we collect every bug report of the chosen project from GitHub's bug tracking system and we analyze every version that is related to any of the bug reports obtained. The source code analysis is carried out by the SourceMeter 3 tool and the bug reports are exported from GitHub by using the GitHub API 4 . More details about this step can be found in our earlier study [1].…”

Section: The Open-source Toolchainmentioning

confidence: 99%

“…More details about this step can be found in our earlier study [1]. Next we build the graph, just like in a previous study [4]. We extended this step to connect the bugs to the selected commits.…”

Section: The Open-source Toolchainmentioning

confidence: 99%

“…These metrics are calculated for files and classes, and even for methods. Continuing this study [4], we assessed the usefulness of the software process metrics in bug prediction. We used bug information of the class and file levels that we published earlier, and we also compared the two sets of predictors (namely, product metrics and process metrics) against each other.…”

Section: Introductionmentioning

confidence: 99%

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An open-source solution for automatic bug database creation

Gyimesi¹

2018

Proceedings of the 10th International Conference on Applied Informatics

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In the field of Software Engineering, a recurring theme is that locating errors and trying to fix them in a software package. Several studies on software bugs have used some kind of bug database to assist their work. These databases usually come from publicly available sources, but some researchers have created their own databases. These kinds of studies are important because the more we know about software bugs, the easier it is to prevent or find and fix them. Software developers being human tend to make mistakes. These may arise due to such factors as a tight deadline, poor design, a change in the specification and lack of experience. This is the reason why it is necessary to support this kind of research with bug databases that represent the defects of real software systems well. Depending on the application of the database, it may contain additional information like bug-related test cases, static source code metrics, design patterns and process metrics. During our previous study we constructed a bug database from Java projects on GitHub. This database contains the faulty source code elements (files and classes) and their static source code metrics. In addition, we used a graph database to compute some process metrics of the buggy source code elements. In this study, we present our BugHunter tool and we improved it by using the graph database to also locate the buggy source code elements. This way, our tool is capable of locating the defective source code elements at the file and class levels, and now even at the method level. Our tool uses Neo4j-a popular open-source graph database engine-and its query language called cypher. The calculations are carried out by running a cypher query, and it makes our method quite flexible. To analyze the given project's source code, we used the free version of the SourceMeter tool. Also, we made our complete tool-chain publicly available as an open-source project on GitHub. This way, the complete system is freely available for studies on software bugs, especially those concerning bug prediction.

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Section: Methodsmentioning

confidence: 99%

“…The definitions of additional graph parts which we did not use in this study can be found in our previous paper [4]. We will now proceed to the bug number calculation.…”

Section: Methodsmentioning

confidence: 99%

Section: The Open-source Toolchainmentioning

confidence: 99%

Section: The Open-source Toolchainmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An open-source solution for automatic bug database creation

Gyimesi¹

2018

Proceedings of the 10th International Conference on Applied Informatics

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Improving Vulnerability Prediction of JavaScript Functions using Process Metrics

Viszkok

Hegedűs

Ferenć

2021

Proceedings of the 16th International Conference on Software Technologies

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Due to the growing number of cyber attacks against computer systems, we need to pay special attention to the security of our software systems. In order to maximize the effectiveness, excluding the human component from this process would be a huge breakthrough. The first step towards this is to automatically recognize the vulnerable parts in our code. Researchers put a lot of effort into creating machine learning models that could determine if a given piece of code, or to be more precise, a selected function, contains any vulnerabilities or not. We aim at improving the existing models, building on previous results in predicting vulnerabilities at the level of functions in JavaScript code using the well-known static source code metrics. In this work, we propose to include several so-called process metrics (e.g., code churn, number of developers modifying a file, or the age of the changed source code) into the set of features, and examine how they affect the performance of the function-level JavaScript vulnerability prediction models. We can confirm that process metrics significantly improve the prediction power of such models. On average, we observed a 8.4% improvement in terms of F-measure (from 0.764 to 0.848), 3.5% improvement in terms of precision (from 0.953 to 0.988) and a 6.3% improvement in terms of recall (from 0.697 to 0.760).

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Vulnerable JavaScript functions detection using stacking of convolutional neural networks

Sheneamer

2024

PeerJ Computer Science

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System security for web-based applications is paramount, and for the avoidance of possible cyberattacks it is important to detect vulnerable JavaScript functions. Developers and security analysts have long relied upon static analysis to investigate vulnerabilities and faults within programs. Static analysis tools are used for analyzing a program’s source code and identifying sections of code that need to be further examined by a human analyst. This article suggests a new approach for identifying vulnerable code in JavaScript programs by using ensemble of convolutional neural networks (CNNs) models. These models use vulnerable information and code features to detect related vulnerable code. For identifying different vulnerabilities in JavaScript functions, an approach has been tested which involves the stacking of CNNs with misbalancing, random under sampler, and random over sampler. Our approach uses these CNNs to detect vulnerable code and improve upon current techniques’ limitations. Previous research has introduced several approaches to identify vulnerable code in JavaScript programs, but often have their own limitations such as low accuracy rates and high false-positive or false-negative results. Our approach addresses this by using the power of convolutional neural networks and is proven to be highly effective in the detection of vulnerable functions that could be used by cybercriminals. The stacked CNN approach has an approximately 98% accuracy, proving its robustness and usability in real-world scenarios. To evaluate its efficacy, the proposed method is trained using publicly available JavaScript blocks, and the results are assessed using various performance metrics. The research offers a valuable insight into better ways to protect web-based applications and systems from potential threats, leading to a safer online environment for all.

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Automatic Calculation of Process Metrics and their Bug Prediction Capabilities

Cited by 3 publications

References 25 publications

An open-source solution for automatic bug database creation

An open-source solution for automatic bug database creation

Improving Vulnerability Prediction of JavaScript Functions using Process Metrics

Vulnerable JavaScript functions detection using stacking of convolutional neural networks

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