A network-centric approach for estimating trust between open source software developers

Sapkota, Hitesh; Murukannaiah, Pradeep K.; Wang, Yi

doi:10.1371/journal.pone.0226281

Cited by 7 publications

(3 citation statements)

References 56 publications

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“…One very influential social factor identified in prior work is trust among contributors [13], [21]- [25]. A reviewer's trust in the author can influence the rigor of evaluation [21], [25] and the decision to merge or reject a contribution [22], [24]. To address this, we've designed a technique to label contributors as trusted or untrusted based on their contribution history for the repository being considered.…”

Section: Contributor Trustmentioning

confidence: 99%

Anomalicious: Automated Detection of Anomalous and Potentially Malicious Commits on GitHub

Gonzalez

Zimmermann

Godefroid

et al. 2021

2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)

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Security is critical to the adoption of open source software (OSS), yet few automated solutions currently exist to help detect and prevent malicious contributions from infecting open source repositories. On GitHub, a primary host of OSS, repositories contain not only code but also a wealth of commitrelated and contextual metadata -what if this metadata could be used to automatically identify malicious OSS contributions?In this work, we show how to use only commit logs and repository metadata to automatically detect anomalous and potentially malicious commits. We identify and evaluate several relevant factors which can be automatically computed from this data, such as the modification of sensitive files, outlier change properties, or a lack of trust in the commit's author. Our tool, Anomalicious, automatically computes these factors and considers them holistically using a rule-based decision model. In an evaluation on a data set of 15 malware-infected repositories, Anomalicious showed promising results and identified 53.33% of malicious commits, while flagging less than 1% of commits for most repositories. Additionally, the tool found other interesting anomalies that are not related to malicious commits in an analysis of repositories with no known malicious commits.Index Terms-anomaly detection, malicious commits, supply chain attacks I .

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Section: Contributor Trustmentioning

confidence: 99%

Anomalicious: Automated Detection of Anomalous and Potentially Malicious Commits on GitHub

Gonzalez

Zimmermann

Godefroid

et al. 2021

2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)

View full text Add to dashboard Cite

show abstract

“…More recently, researchers have studied complex, emotionally charged psychological concepts such as toxicity in issue reports [16,26], and confusion in code reviews [14]. Likewise affective trust between developers of a project was investigated in pull requests and commit comments [3,11,31,32].…”

mentioning

confidence: 99%

Towards Understanding Emotions in Informal Developer Interactions: A Gitter Chat Study

Sajadi,

Damevski,

Chatterjee

2023

Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Enginee

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Emotions play a significant role in teamwork and collaborative activities like software development. While researchers have analyzed developer emotions in various software artifacts (e.g., issues, pull requests), few studies have focused on understanding the broad spectrum of emotions expressed in chats. As one of the most widely used means of communication, chats contain valuable information in the form of informal conversations, such as negative perspectives about adopting a tool. In this paper, we present a dataset of developer chat messages manually annotated with a wide range of emotion labels (and sub-labels), and analyze the type of information present in those messages. We also investigate the unique signals of emotions specific to chats and distinguish them from other forms of software communication. Our findings suggest that chats have fewer expressions of Approval and Fear but more expressions of Curiosity compared to GitHub comments. We also notice that Confusion is frequently observed when discussing programming-related information such as unexpected software behavior. Overall, our study highlights the potential of mining emotions in developer chats for supporting software maintenance and evolution tools. CCS CONCEPTS• Software and its engineering → Collaboration in software development.

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“…As standards indicate, the objects inherited serializable capabilities to be transmittable over P2P TCP-Sockets (Sapkota et al, 2019). However, in this case, the mining process was tested without a cloud environment to measure the pure mining algorithm e ciency, without network inter-processes intervention.…”

mentioning

confidence: 99%

Blockchain Mining: Understanding its Difficulty in Terms of Hashing Algorithm Efficiency

Martínez

2022

Preprint

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Blockchain is an emerging technology that offers great advantages such as global distribution of information and data immutability over time, but its mining process is known to be time consuming and highly resource demanding. There are different algorithms to mine blocks, being able to operate at different levels of complexity and also incurring different compute costs. This study used a custom and simple implementation of the MessageDigest Java class to evaluate the performance of MD5 version 2, SHA2, and SHA3 (the last two in modalities of 256 and 512-bits) when automatically creating a chain of a thousand blocks, measuring its average time for generation and proof of work verification, alongside to the count of incurred iterations. The SHA3 methods produced stronger hash values but performed slower than MD5 and SHA2, and the complexity level of 4 characters offered the best security level in terms of iteration count versus calculation time. Complexity levels of 5 and 6 characters offered greater security but with a drastic performance reduction, which is proper for critical security systems that do not process high volumes of information. These results are useful for those small to medium organizations that are planning to implement blockchain technologies and need a parameter for understanding exactly what ‘mining cost’ really means in terms of compute costs.

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A network-centric approach for estimating trust between open source software developers

Cited by 7 publications

References 56 publications

Anomalicious: Automated Detection of Anomalous and Potentially Malicious Commits on GitHub

Anomalicious: Automated Detection of Anomalous and Potentially Malicious Commits on GitHub

Towards Understanding Emotions in Informal Developer Interactions: A Gitter Chat Study

Blockchain Mining: Understanding its Difficulty in Terms of Hashing Algorithm Efficiency

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