Research Questions How does anomaly detection using knowledge graphs compare with existing approaches for detecting suspicious or wrong behavior in software developers? What factors can be used to measure the accuracy and precision of anomaly detection models based on knowledge graphs? How can knowledge graph-based anomaly detection be applied to detect wrong or unusual behavior in software developers?

Methodology

Data Collection

The data for this thesis will be collected from multiple sources, including software development tools, such as GitHub and Stack Overflow, and various software development blogs and forums. This data will be collected using web scraping techniques, allowing for the capture of relevant information.

Data Analysis

Once the data has been collected, it will then be analyzed to identify any unusual behavior of software developers. In order to do this, a knowledge graph will be created, which contains relationships between different entities, such as developers, technologies, topics, and other resources. When using this graph, machine learning algorithms will be applied to detect any anomalies within the data. In addition to using a knowledge graph and machine learning algorithms, natural language processing techniques will also be used to analyze text-based data and detect any patterns that may indicate unusual behavior. This will include analyzing text from blog posts and forum discussions and code snippets from open-source repositories. Finally, statistical methods will be used to validate the analysis results further and draw conclusions about the data.

The Implementation Plan of the Research Methods

The implementation plan for this methodology will involve the following steps:

• Collecting data from software development tools, blogs, and forums using web scraping techniques.
• Creating a knowledge graph with the collected data to visualize relationships between different entities.
• Applying machine learning algorithms on the knowledge graph to identify anomalies and patterns which may indicate suspect or unusual behavior of software developers.
• Applying natural language processing techniques on text-based data to detect patterns that may indicate unusual behavior.
• Applying statistical methods to validate the analysis results further and draw conclusions about the data.

Results

The results of this study will be evaluated by analyzing the effectiveness of the anomaly detection model in detecting wrong or unusual behavior of software developers. The evaluation will be based on how accurately the model can identify anomalies in the knowledge graphs used to train the system. The model’s accuracy can be measured by calculating the number of false positives and negatives that occur while using the model (Ko et al., 2021). The model’s performance can also be tested with other datasets to determine its generalizability. Once the model’s effectiveness is determined, this study’s results will be discussed in terms of their implications for the software development community. Specifically, the findings from this research could provide software developers with an automated tool for detecting possible suspicious activity promptly. This could help prevent security threats from occurring before they cause damage to an organization or individual. Furthermore, the results of this research could lead to further improvements in the technology related to security and knowledge graphs, allowing for more accurate models to be developed in the future. Finally, this research could provide insight into techniques that could be used in other security areas, such as financial fraud or data leakage detection. All these aspects should be addressed to ensure that this proposal fulfills its primary goal, which is to enhance security in software development.

Conclusion and direction of Future Research

The study will explore the possibility of using knowledge graphs to detect wrong or unusual behavior in software developers. Through research, people will see the potential of utilizing this technology to monitor and manage software development. However, more research is needed to determine its effectiveness in this field. Based on the reviewed literature, knowledge graphs have the potential to be an effective tool for identifying unusual behavior within software development processes. By analyzing the data collected from a knowledge graph, developers can identify anomalies and react accordingly to mitigate risks associated with such anomalies. For future research, it is suggested that further work should be done to investigate the use of knowledge graphs in other fields, such as security and fraud detection. Additionally, further experiments should be conducted to test the accuracy and effectiveness of knowledge graphs when applied to different datasets. Finally, further studies should be conducted to determine how well knowledge graphs can scale to larger datasets and their limitations in certain situations. With these studies, it is possible to understand better how knowledge graphs c