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The presentation was prepared for the University of Adelaide School of Computer Science Research Seminar Series. See the slides to know
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- what are the key challenges in this domain?
- how software architecture can play a role in improving the design decision of security orchestration and automation platform?
Multi-vocal Review of security orchestrationChadni Islam
The document summarizes a literature review on security orchestration. The review analyzed papers from various sources to understand different aspects of security orchestration such as definitions, challenges it addresses, proposed solutions, adoption practices, and architectural considerations. Key findings include that security orchestration aims to integrate disparate security tools, automate incident response workflows, and bridge the gap between detection and response. It addresses issues like lack of interoperability, skills shortage and inefficient manual processes. Taxonomies of proposed solutions and open challenges in technology, people and processes are also discussed.
The Gap Between Academic Research and Industrial Practice in Software TestingZoltan Micskei
In software engineering, there is always a gap between the current research topics and the everyday industrial practice. However, in my experience this gap is much wider in software testing, e.g. advanced testing techniques seldom reach everyday testers. This gap can be attributed to several factors, the talk will highlight education and tools from the possible causes. In order to illustrate this gap the talk will collect and compere the topics of recent academic and industrial testing related conferences. My goal is to offer a glimpse into recent software testing research topics to practitioners, and start a discussion whether there is really a wide gap between academy and industry.
How to Extend Security and Compliance Within BoxElastica Inc.
Choosing an enterprise-class file sharing service such as Box is a great first step in safely migrating to the cloud. However even with the most robust service, enterprise organizations are still responsible for how their users take advantage of the service, what sensitive content they upload and share, and potential damage due to compromised user credentials.
In this on-demand webcast Eric Andrews, Elastica VP of Marketing, will discuss:
• What base level security Box provides
• Best practices in identifying sensitive, shared content that may violate compliance policies (PCI, PHI, PII, etc.)
• Best practices in using data science to uncover risky or anomalous behavior
US AI Safety Institute and Trustworthy AI Details.Bob Marcus
This is a discussion of the possible role of the US AI Safety Institute in regulating Generative AI. It includes External Red Team Testing and an Incident Tracking Database.
The document discusses software management and the evolution of approaches to software development. It covers the following key points:
- Traditional "waterfall" models of software development had drawbacks like late risk resolution and focus on documentation over collaboration.
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- Improving software economics involves optimizing factors like size, process, personnel skills, tools/environments, and quality requirements. Techniques like reuse, object orientation, and automated testing can help compress schedules and reduce costs.
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The document summarizes a school penetration testing project conducted by UDomain. They identified over 1,700 vulnerabilities across 10 school websites, including 20,000+ records of personal data. Critical vulnerabilities included SQL injection, XSS, and passwords in plaintext. Recommendations included more regular scanning, patching of outdated systems, and reliance on secure vendor solutions. UDomain demonstrated SQL injection techniques and explained their security services and qualifications.
Tools for Building Confidence in Using Simulation To Inform or Replace Real-W...Kieran Alden
Slides from talk delivered at the University of Michigan, 8th October 2018, that introduces the software tools developed to support the modelling of biological systems, produced at the University of York.
Architecture centric support for security orchestration and automationChadni Islam
The presentation was prepared for the University of Adelaide School of Computer Science Research Seminar Series. See the slides to know
- what is security orchestration?
- what are the key challenges in this domain?
- how software architecture can play a role in improving the design decision of security orchestration and automation platform?
Multi-vocal Review of security orchestrationChadni Islam
The document summarizes a literature review on security orchestration. The review analyzed papers from various sources to understand different aspects of security orchestration such as definitions, challenges it addresses, proposed solutions, adoption practices, and architectural considerations. Key findings include that security orchestration aims to integrate disparate security tools, automate incident response workflows, and bridge the gap between detection and response. It addresses issues like lack of interoperability, skills shortage and inefficient manual processes. Taxonomies of proposed solutions and open challenges in technology, people and processes are also discussed.
The Gap Between Academic Research and Industrial Practice in Software TestingZoltan Micskei
In software engineering, there is always a gap between the current research topics and the everyday industrial practice. However, in my experience this gap is much wider in software testing, e.g. advanced testing techniques seldom reach everyday testers. This gap can be attributed to several factors, the talk will highlight education and tools from the possible causes. In order to illustrate this gap the talk will collect and compere the topics of recent academic and industrial testing related conferences. My goal is to offer a glimpse into recent software testing research topics to practitioners, and start a discussion whether there is really a wide gap between academy and industry.
How to Extend Security and Compliance Within BoxElastica Inc.
Choosing an enterprise-class file sharing service such as Box is a great first step in safely migrating to the cloud. However even with the most robust service, enterprise organizations are still responsible for how their users take advantage of the service, what sensitive content they upload and share, and potential damage due to compromised user credentials.
In this on-demand webcast Eric Andrews, Elastica VP of Marketing, will discuss:
• What base level security Box provides
• Best practices in identifying sensitive, shared content that may violate compliance policies (PCI, PHI, PII, etc.)
• Best practices in using data science to uncover risky or anomalous behavior
US AI Safety Institute and Trustworthy AI Details.Bob Marcus
This is a discussion of the possible role of the US AI Safety Institute in regulating Generative AI. It includes External Red Team Testing and an Incident Tracking Database.
The document discusses software management and the evolution of approaches to software development. It covers the following key points:
- Traditional "waterfall" models of software development had drawbacks like late risk resolution and focus on documentation over collaboration.
- Newer agile approaches emphasize iterative development, early delivery of working software, and continuous improvement based on feedback.
- Improving software economics involves optimizing factors like size, process, personnel skills, tools/environments, and quality requirements. Techniques like reuse, object orientation, and automated testing can help compress schedules and reduce costs.
- Effective project management requires skills like hiring, communication, decision making, team building, and adapting to changes over time. Developing high
The document summarizes a school penetration testing project conducted by UDomain. They identified over 1,700 vulnerabilities across 10 school websites, including 20,000+ records of personal data. Critical vulnerabilities included SQL injection, XSS, and passwords in plaintext. Recommendations included more regular scanning, patching of outdated systems, and reliance on secure vendor solutions. UDomain demonstrated SQL injection techniques and explained their security services and qualifications.
Tools for Building Confidence in Using Simulation To Inform or Replace Real-W...Kieran Alden
Slides from talk delivered at the University of Michigan, 8th October 2018, that introduces the software tools developed to support the modelling of biological systems, produced at the University of York.
Doing Science Properly In The Digital Age - Rutgers SeminarNeil Chue Hong
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This document is a resume for Tirumala Reddy K, a software developer and web developer currently pursuing an M.S. in Computer Science from the University of Missouri Kansas City with expected graduation in May 2015. Reddy has work experience as a software developer at Infotech Enterprises Ltd from July 2012 to December 2013 and as a web developer at the University of Missouri Kansas City from Summer 2014 to Fall 2014. Reddy's education includes a B.Tech in Electronics and Communications Engineering from Sri Venkateswara University College of Engineering obtained in May 2012 with a percentage of 82.7%.
DevSecOps aims to integrate security practices into DevOps workflows to deliver value faster and safer. It addresses challenges like keeping security practices aligned with continuous delivery models and empowered DevOps teams. DevSecOps incorporates security checks and tools into development pipelines to find and fix issues early. This helps prevent breaches like the 2017 Equifax hack, which exploited a known vulnerability. DevSecOps promotes a culture of collaboration, shared responsibility, and proactive security monitoring throughout the software development lifecycle.
Executing on the promise of the Internet of Things (IoT)Dell World
The document discusses the Internet of Things (IoT) and how analytics can be used to extract insights from IoT data. It describes how IoT involves connecting sensors and devices to collect and transmit data for tracking, analysis, and actions. It also discusses how IoT analytics can reduce risks, optimize customer service, and couple device data with other data sources. Additionally, the document provides examples of how Dell Statistica advanced analytics software and Dell IoT solutions have helped customers in various industries like manufacturing, healthcare, utilities, and more.
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Agile Methods Cost of Quality: Benefits of Testing Early & OftenDavid Rico
This document discusses the benefits of testing early and often using agile methods. It summarizes that agile testing is done continuously and automated through frequent integration, which finds defects much faster than traditional late testing. This reduces costs and risks while improving quality.
Murali Krishnan Narayanan is a software quality professional with over 8 years of experience in functional testing. He has extensive experience in testing web and mobile applications, defining test plans and cases, executing tests, and reporting on results. He is proficient in defect tracking tools like Jira and has worked on projects in various domains like publishing, aviation, gaming, ecommerce, and elearning.
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Software Testing: Issues and Challenges of Artificial Intelligence & Machine ...gerogepatton
The history of Artificial Intelligence and Machine Learning dates back to 1950’s. In recent years, there has been an increase in popularity for applications that implement AI and ML technology. As with traditional development, software testing is a critical component of an efficient AI/ML application. However, the approach to development methodology used in AI/ML varies significantly from traditional development. Owing to these variations, numerous software testing challenges occur. This paper aims to recognize and to explain some of the biggest challenges that software testers face in dealing with AI/ML applications. For future research, this study has key implications. Each of the challenges outlined in this paper is ideal for further investigation and has great potential to shed light on the way to more productive software testing strategies and methodologies that can be applied to AI/ML applications.
Software Testing: Issues and Challenges of Artificial Intelligence & Machine ...gerogepatton
The history of Artificial Intelligence and Machine Learning dates back to 1950’s. In recent years, there has been an increase in popularity for applications that implement AI and ML technology. As with traditional development, software testing is a critical component of an efficient AI/ML application. However, the approach to development methodology used in AI/ML varies significantly from traditional development. Owing to these variations, numerous software testing challenges occur. This paper aims to recognize and to explain some of the biggest challenges that software testers face in dealing with AI/ML applications. For
future research, this study has key implications. Each of the challenges outlined in this paper is ideal for further investigation and has great potential to shed light on the way to more productive software testing strategies and methodologies that can be applied to AI/ML applications.
SOFTWARE TESTING: ISSUES AND CHALLENGES OF ARTIFICIAL INTELLIGENCE & MACHINE ...ijaia
The history of Artificial Intelligence and Machine Learning dates back to 1950’s. In recent years, there has
been an increase in popularity for applications that implement AI and ML technology. As with traditional
development, software testing is a critical component of an efficient AI/ML application. However, the
approach to development methodology used in AI/ML varies significantly from traditional development.
Owing to these variations, numerous software testing challenges occur. This paper aims to recognize and
to explain some of the biggest challenges that software testers face in dealing with AI/ML applications. For
future research, this study has key implications. Each of the challenges outlined in this paper is ideal for
further investigation and has great potential to shed light on the way to more productive software testing
strategies and methodologies that can be applied to AI/ML applications.
Shivani Jain seeks a position as an IT professional to utilize her technical and intellectual abilities. She has a M.Tech in Information Technology from GGS Indraprastha University with 76.03% and a B.Tech in Information Technology from HMR Institute of Technology and Management with 74.2%. Her experience includes research work at ICAR-Indian Agricultural Statistical Research Institute and teaching at Mahan Institute of Technologies. She is proficient in languages like Java, C++, HTML, and technologies like CloudAnalyst and CloudSim.
AI for Software Testing Excellence in 2024Testgrid.io
Revolutionize your testing process with Artificial Intelligence. This book explores how AI is transforming software testing, delivering faster, more efficient, and more effective methods.
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Topics covered include:
How AppSec processes are really quality processes
How software bugs are really security vulnerabilities
How to apply coding standards as part of a continuous testing process to prevent defects from affecting the safety, security, and reliability of your applications
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This document discusses cloning an organization to allow testing and manipulation without affecting the original site. It defines cloning as creating an exact copy that can be used for tasks without risk to the original. Types of clones include the frontend design, backend design, and database. Benefits of cloning for software testing are that it is cost-effective, improves security and product quality, and increases customer satisfaction. The document then discusses various software testing types, reverse engineering, and software development life cycles like waterfall, RAD, spiral, V-model, incremental, agile, iterative, big bang and prototype models. The conclusion is that cloning can help test and learn new features without interrupting the original organization's data and business.
This document discusses the benefits of testing early and often using agile methods. It begins with background on the author and then discusses challenges with traditional project management approaches. Key benefits of agile testing highlighted include finding defects much earlier, improving productivity, and increasing project success rates. The document provides an overview of agile testing practices and how they improve the testing workflow. It also discusses how agile testing approaches can help control costs and improve overall project quality.
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This document discusses the benefits of testing early and often using agile methods. It summarizes that agile testing is done continuously and automated through frequent integration, which finds defects much faster than traditional late testing. This reduces costs and risks while improving quality.
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The document discusses different levels of analytics maturity from reactive operational reporting to prescriptive analytics. It provides examples of analytics applications including predicting top talent retention and identifying abnormal patterns in organizational structures. The second half of the document focuses on building a state-of-the-art analytics system, outlining key components like data integration, machine learning pipelines for feature extraction, model training and evaluation, and publishing results.
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development, software testing is a critical component of an efficient AI/ML application. However, the
approach to development methodology used in AI/ML varies significantly from traditional development.
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The UI5 tooling is the development and build tooling of UI5. It is built in a modular and extensible way so that it can be easily extended by your needs. This session will showcase various tooling extensions which can boost your development experience by far so that you can really work offline, transpile your code in your project to use even newer versions of EcmaScript (than 2022 which is supported right now by the UI5 tooling), consume any npm package of your choice in your project, using different kind of proxies, and even stitching UI5 projects during development together to mimic your target environment.
What is Augmented Reality Image Trackingpavan998932
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See My Other Reviews Article:
(1) AI Genie Review: https://sumonreview.com/ai-genie-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
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A Study of Variable-Role-based Feature Enrichment in Neural Models of CodeAftab Hussain
Understanding variable roles in code has been found to be helpful by students
in learning programming -- could variable roles help deep neural models in
performing coding tasks? We do an exploratory study.
- These are slides of the talk given at InteNSE'23: The 1st International Workshop on Interpretability and Robustness in Neural Software Engineering, co-located with the 45th International Conference on Software Engineering, ICSE 2023, Melbourne Australia
Top Features to Include in Your Winzo Clone App for Business Growth (4).pptxrickgrimesss22
Discover the essential features to incorporate in your Winzo clone app to boost business growth, enhance user engagement, and drive revenue. Learn how to create a compelling gaming experience that stands out in the competitive market.
GraphSummit Paris - The art of the possible with Graph TechnologyNeo4j
Sudhir Hasbe, Chief Product Officer, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Odoo ERP software
Odoo ERP software, a leading open-source software for Enterprise Resource Planning (ERP) and business management, has recently launched its latest version, Odoo 17 Community Edition. This update introduces a range of new features and enhancements designed to streamline business operations and support growth.
The Odoo Community serves as a cost-free edition within the Odoo suite of ERP systems. Tailored to accommodate the standard needs of business operations, it provides a robust platform suitable for organisations of different sizes and business sectors. Within the Odoo Community Edition, users can access a variety of essential features and services essential for managing day-to-day tasks efficiently.
This blog presents a detailed overview of the features available within the Odoo 17 Community edition, and the differences between Odoo 17 community and enterprise editions, aiming to equip you with the necessary information to make an informed decision about its suitability for your business.
Takashi Kobayashi and Hironori Washizaki, "SWEBOK Guide and Future of SE Education," First International Symposium on the Future of Software Engineering (FUSE), June 3-6, 2024, Okinawa, Japan
Utilocate offers a comprehensive solution for locate ticket management by automating and streamlining the entire process. By integrating with Geospatial Information Systems (GIS), it provides accurate mapping and visualization of utility locations, enhancing decision-making and reducing the risk of errors. The system's advanced data analytics tools help identify trends, predict potential issues, and optimize resource allocation, making the locate ticket management process smarter and more efficient. Additionally, automated ticket management ensures consistency and reduces human error, while real-time notifications keep all relevant personnel informed and ready to respond promptly.
The system's ability to streamline workflows and automate ticket routing significantly reduces the time taken to process each ticket, making the process faster and more efficient. Mobile access allows field technicians to update ticket information on the go, ensuring that the latest information is always available and accelerating the locate process. Overall, Utilocate not only enhances the efficiency and accuracy of locate ticket management but also improves safety by minimizing the risk of utility damage through precise and timely locates.
openEuler Case Study - The Journey to Supply Chain Security
Data Quality for Software Vulnerability Dataset
1. The University of Adelaide
Data Quality for Software Vulnerability
Datasets
Centre of Research on Engineering Software Technologies (CREST - @crest_uofa)
School of Computer Science, The University of Adelaide, Australia
Cyber Security Cooperative Research Centre, Australia
The 45th International Conference on Software Engineering (ICSE ‘23)
May 17, 2023
Roland Croft
roland.croft@adelaide.edu.au
M. Ali Babar
ali.babar@adelaide.edu.au
Mehdi Kholoosi
mehdi.kholoosi@adelaide.edu.au
2. Growth of AI
The University of Adelaide Slide 2
AI is beginning to shape
software development and
software quality assurance.
3. Software Vulnerability Prediction
The University of Adelaide Slide 3
• Utilise AI to improve automation and effectiveness of vulnerability detection.
• Use knowledge from previous examples to automatically learn vulnerable patterns.
Previous known Vulnerabilities
Machine Learning
Prediction
4. Software Vulnerability Prediction
The University of Adelaide Slide 4
• Utilise AI to improve automation and effectiveness of vulnerability detection.
• Use knowledge from previous examples to automatically learn vulnerable patterns.
Previous known Vulnerabilities
Machine Learning
Prediction
Data is the core
component of any
data-driven pipeline:
“Garbage In, Garbage Out”
5. Software Vulnerability Datasets
The University of Adelaide Slide 5
Weak
Supervision
1. Vulnerability Reports
2. Development Commit
Logs
3. Static Analysis Tools
4. Synthetic Data
6. Research Objective
The University of Adelaide Slide 6
Aim
Outcomes
Inform the state of software
vulnerability data quality and the
reliability of downstream tasks.
1
Enable automated data cleaning
frameworks to improve data quality
and downstream tasks.
2
To gain deep understanding into
the nature of data quality for
software vulnerability datasets.
8. Research Design
The University of Adelaide Slide 8
Data Quality Attributes
Accuracy
1
Completeness
4
Uniqueness
2
Consistency
3
Currentness
5
9. Research Design
The University of Adelaide Slide 9
Labelling Heuristic: Selected Dataset:
Security Big-Vul
Developer Devign
Tool D2A
Synthetic Juliet Test Suite
10. Research Design
The University of Adelaide Slide 10
Inspect change in model
performance caused by
attempting to reduce data
quality issues.
11. Findings - Accuracy
The University of Adelaide Slide 11
“The degree to which the data has attributes that correctly represent the
true value of the intended attribute of a concept or event in a specific
context of use.”
Big-Vul 54.3%
Devign 80.0%
28.6%
D2A
100%
Juliet
Manually inspect
label correctness
-50%
Lower performance
on true labels
-29%
-80%
12. Findings - Uniqueness
The University of Adelaide Slide 12
“The degree to which there is no duplication in records.”
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Security Developer Tool Synthetic
Model Performance with and without
duplicates
Original No duplicates
-13.9%
-81.7%
-10.4%
Big-Vul 83.0%
Devign 89.9%
2.1%
D2A
16.3%
Juliet
13. Key Takeaways
The University of Adelaide Slide 13
State of the art software vulnerability datasets are imperfect.
Data quality significantly affects the performance of downstream software security
models.
We need better cleaning methods or more robust models to ensure reliability and
effective data driven software security.
Dataset Accuracy Uniqueness Consistency Completeness Currentness
Big-Vul
0.543 0.830 0.999 0.824 0.761
Devign
0.800 0.899 0.991 0.944 0.811
D2A
0.286 0.021 0.531 0.981 0.844
Juliet
1 0.163 0.750 1 NA
Dataset data
quality values
Editor's Notes
Self-Introduction. I will be presenting our paper “Data Quality for Software Vulnerability Datasets.”
Many of us have been witnessing the huge growth in AI over the last few years, and the software engineering community is no exception. Many organizations are beginning to harness the power of AI to provide intelligent tools that assist with software development and quality assurance. For instance, ChatGPT has blown away the world with its remarkable capabilities for programming and code comprehension. A properly trained model is powerful, and it allows us to effectively automate tasks that we’d otherwise find challenging or time-consuming.
Now in the software security domain, there’s actually a lot of really hard difficult time consuming tasks we’d love to automate. We’ll focus on software vulnerability detection. Vulnerabilities are security weakness in the code that can cause catastrophic consequences when exploited by attackers. The issue is however that they are hard to spot, and it can take developers years and years to review and test every single piece of code. This is where AI comes in. AI has shown much promise towards improving the automation and effectiveness of software vulnerability detection. The basic idea of these solutions is that we use historical records of vulnerability examples to train learning-based models that can automatically detect vulnerable patterns. This example here depicts a simple but dangerous buffer overflow, which we can show to our model, and after it works its magic it can theoretically spot the vulnerability in future.
Now as you may have guessed from the title, this talk isn’t actually going to be about this little amazing machine learning model here. No, it’s going to be about the data. Why? Because the data is actually rather important. A fundamental concept in computer science states that the quality of outputs of a system is dictated by the quality of its inputs. This concept is beautifully summarized by the saying “garbage in, garbage out.” The data is important.
So how do we get a nice cleanly labeled vulnerability dataset? Well this is actually extremely difficult. For traditional supervised learning problems, we might get some subject matter expert to hand label the data. But we can’t really do this for vulnerability data as it’s extremely scarce and complex. We instead use weak supervion to obtain some higher-level indicators to produce our labels. I’ll go through each of the four main ways we can do this.
Firstly, over the lifetime of a project, we naturally detect and report vulnerabilities through testing and use. For open source software, these reports are often documented in security advisories. We can attempt to trace the information contained in these reports back to the original code, and this gives us an idea of which code snippets were vulnerable.
The second approach is very similar to the last one, but rather than going through a third party vulnerability database, we can just look at the development history directly for commits describing vulnerability fixes.
However, these two sources only provide label indicators for known vulnerabilities. This means we get very small datasets in practice. This is where our third approach comes in. What if we didn’t have to wait for a developer to spot a vulnerability in order to know where it is. Well we can use some automatic tools to scan the code and tell us where the vulnerabilities. Of course this heavily relies on how reliable are tool is.
Finally, to overcome these uncertainties, we can kind of just cheat and just simply make the data up. This is called synthetic data, where we automatically create examples of code that we know to be vulnerable or not vulnerable, using known patterns.
Now none of these data collection approaches are perfect unfortunately. As each of these data sources is using relatively weak label indicators, they exhibit weakness and produce lower quality datasets than traditional supervision. But despite the importance of the data, and the difficulties we have in repairing it, we’ve found the data quality to actually be a rather ill-considered concept in software security, until now.
Hence, our goal is to gather a deep understanding of the data quality of existing software vulnerability datasets. We aim to do this for two major reasons. Firstly, our findings will help inform and raise awareness of the importance of data quality for data-driven software security research, and the impacts that data quality issues can have. Secondly, by gathering deep knowledge of the nature of data quality issues, we can learn how to prevent and overcome then. Ensuring data quality is key to enabling reliable and effective solutions for AI-based software security.
To achieve our aims, we conduct an empirical study using a simple 3 step process.
Firstly, we identify the data characteristics that we will examine. We use the ISO/IEC 25012 data quality standard to obtain 5 inherent data quality attributes: accuracy, uniqueness, consistency, completeness, and currentness. I’ll go over the definitions of these during the findings.
Secondly, we measure each of these attributes on the existing state of the art datasets. We applied a quality selection criteria to collect one dataset for each of the 4 labeling heuristics that we previously outlined. The four datasets are called Big-Vul, Devign, D2A, and the Juliet Test Suite.
Thirdly, we validated the actual importance and relevance of each attribute for our use case of software vulnerability prediction. We took state of the art prediction models and trained them on each of our datasets. Then we see how the performance changed when we attempted to mitigate or remove the data quality issues observed. Let’s get into it.
Thirdly, we validated the actual importance and relevance of each attribute for our use case of software vulnerability prediction. We took state of the art prediction models and trained them on each of our datasets. Then we see how the performance changed when we attempted to mitigate or remove the data quality issues observed. Now due to the time constraints of this presentation, I’m only going to go over our findings for the first two data attributes, but our full findings are in the paper.
It’s an expectation that when we’re working with a dataset, that the data labels are actually correct, and this is what the accuracy attribute measures. For vulnerability data we are essentially checking whether our collected vulnerabilities are actually vulnerabilities. Now to measure this, through some quite painstaking efforts, we manually examined the labeling mechanisms that assigned the data points and verified each data point as correct or not. We found that some vulnerability datasets, don’t actually do a very good job of containing vulnerabilities. The worst case is for the tool based dataset, in which only 28.6% of the data was accurate, as static analysis tools have very high false positive rates. More importantly though, these label inaccuracies have catastrophic consequences when we train the models with this data. When we evaluated our models using our manually verified data points, the performance dropped significantly, up to 80%. This is as the models are learning the wrong patterns in the training data. On the other hand, synthetic data is largely correct as the vulnerabilities are specifically crafted for these purposes, rather than collected post-hoc.
Uniqueness is defined as the degree to which there is no duplication in records. Duplication for code datasets can actually be quite common. The same piece of code can get flagged multiple times or at different stages of development. The tool-based and synthetic datasets take this to the extreme however. Only 2.1% of the dataset contained unique values in the worst case.
Duplication can be a significant problem in machine learning due to data leakage. If the validation or test set that is used to guide the learning process contains samples that the model has already seen, well its like we’re letting our model cheat on the test, and this wildly inflates the performance. We can see this in our experiments, where the model performance decreases after we remove duplicates. This is important, as we’re now getting a truer indication of our model performance.
Looking at our findings as a whole, all the examined datasets exhibited issues in various data quality aspects. Other than the synthetic dataset, none of the labeling heuristic are able to produce actually very accurate labels, which means our models are just learning the wrong things. Furthermore, the larger datasets, the ones that don’t rely on reported vulnerabilities, have huge problems with duplication and consistency. Current state of the art datasets are imperfect. What’s more, is that these issues can’t be ignored, as they have significant impacts on the tasks that rely on this data. To move towards the future, to enable data-driven intelligent methods for software security, we need to make these datasets better and overcome these challenges.