Biblio

Context: Software security is an imperative aspect of software quality. Early detection of vulnerable code during development can better ensure the security of the codebase and minimize testing efforts. Although traditional software metrics are used for early detection of vulnerabilities, they do not clearly address the granularity level of the issue to precisely pinpoint vulnerabilities. The goal of this study is to employ method-level traceable patterns (nano-patterns) in vulnerability prediction and empirically compare their performance with traditional software metrics. The concept of nano-patterns is similar to design patterns, but these constructs can be automatically recognized and extracted from source code. If nano-patterns can better predict vulnerable methods compared to software metrics, they can be used in developing vulnerability prediction models with better accuracy. Aims: This study explores the performance of method-level patterns in vulnerability prediction. We also compare them with method-level software metrics. Method: We studied vulnerabilities reported for two major releases of Apache Tomcat (6 and 7), Apache CXF, and two stand-alone Java web applications. We used three machine learning techniques to predict vulnerabilities using nano-patterns as features. We applied the same techniques using method-level software metrics as features and compared their performance with nano-patterns. Results: We found that nano-patterns show lower false negative rates for classifying vulnerable methods (for Tomcat 6, 21% vs 34.7%) and therefore, have higher recall in predicting vulnerable code than the software metrics used. On the other hand, software metrics show higher precision than nano-patterns (79.4% vs 76.6%). Conclusion: In summary, we suggest developers use nano-patterns as features for vulnerability prediction to augment existing approaches as these code constructs outperform standard metrics in terms of prediction recall.

Software vulnerabilities often remain hidden until an attacker exploits the weak/insecure code. Therefore, testing the software from a vulnerability discovery perspective becomes challenging for developers if they do not inspect their code thoroughly (which is time-consuming). We propose that vulnerability prediction using certain software metrics can support the testing process by identifying vulnerable code-components (e.g., functions, classes, etc.). Once a code-component is predicted as vulnerable, the developers can focus their testing efforts on it, thereby avoiding the time/effort required for testing the entire application. The current paper presents a study that compares how software metrics perform as vulnerability predictors for software projects developed in two different languages (Java vs Python). The goal of this research is to analyze the vulnerability prediction performance of software metrics for different programming languages. We designed and conducted experiments on security vulnerabilities reported for three Java projects (Apache Tomcat 6, Tomcat 7, Apache CXF) and two Python projects (Django and Keystone). In this paper, we focus on a specific type of code component: Functions. We apply Machine Learning models for predicting vulnerable functions. Overall results show that software metrics-based vulnerability prediction is more useful for Java projects than Python projects (i.e., software metrics when used as features were able to predict Java vulnerable functions with a higher recall and precision compared to Python vulnerable functions prediction).

Ensuring software security is essential for developing a reliable software. A software can suffer from security problems due to the weakness in code constructs during software development. Our goal is to relate software security with different code constructs so that developers can be aware very early of their coding weaknesses that might be related to a software vulnerability. In this study, we chose Java nano-patterns as code constructs that are method-level patterns defined on the attributes of Java methods. This study aims to find out the correlation between software vulnerability and method-level structural code constructs known as nano-patterns. We found the vulnerable methods from 39 versions of three major releases of Apache Tomcat for our first case study. We extracted nano-patterns from the affected methods of these releases. We also extracted nano-patterns from the non-vulnerable methods of Apache Tomcat, and for this, we selected the last version of three major releases (6.0.45 for release 6, 7.0.69 for release 7 and 8.0.33 for release 8) as the non-vulnerable versions. Then, we compared the nano-pattern distributions in vulnerable versus non-vulnerable methods. In our second case study, we extracted nano-patterns from the affected methods of three vulnerable J2EE web applications: Blueblog 1.0, Personalblog 1.2.6 and Roller 0.9.9, all of which were deliberately made vulnerable for testing purpose. We found that some nano-patterns such as objCreator, staticFieldReader, typeManipulator, looper, exceptions, localWriter, arrReader are more prevalent in affected methods whereas some such as straightLine are more vivid in non-affected methods. We conclude that nano-patterns can be used as the indicator of vulnerability-proneness of code.

Software security is an important aspect of ensuring software quality. Early detection of vulnerable code during development is essential for the developers to make cost and time effective software testing. The traditional software metrics are used for early detection of software vulnerability, but they are not directly related to code constructs and do not specify any particular granularity level. The goal of this study is to help developers evaluate software security using class-level traceable patterns called micro patterns to reduce security risks. The concept of micro patterns is similar to design patterns, but they can be automatically recognized and mined from source code. If micro patterns can better predict vulnerable classes compared to traditional software metrics, they can be used in developing a vulnerability prediction model. This study explores the performance of class-level patterns in vulnerability prediction and compares them with traditional class-level software metrics. We studied security vulnerabilities as reported for one major release of Apache Tomcat, Apache Camel and three stand-alone Java web applications. We used machine learning techniques for predicting vulnerabilities using micro patterns and class-level metrics as features. We found that micro patterns have higher recall in detecting vulnerable classes than the software metrics.

Software security is an important aspect of ensuring software quality. The goal of this study is to help developers evaluate software security using traceable patterns and software metrics during development. The concept of traceable patterns is similar to design patterns but they can be automatically recognized and extracted from source code. If these patterns can better predict vulnerable code compared to traditional software metrics, they can be used in developing a vulnerability prediction model to classify code as vulnerable or not. By analyzing and comparing the performance of traceable patterns with metrics, we propose a vulnerability prediction model. This study explores the performance of some code patterns in vulnerability prediction and compares them with traditional software metrics. We use the findings to build an effective vulnerability prediction model. We evaluate security vulnerabilities reported for Apache Tomcat, Apache CXF and three stand-alone Java web applications. We use machine learning and statistical techniques for predicting vulnerabilities using traceable patterns and metrics as features. We found that patterns have a lower false negative rate and higher recall in detecting vulnerable code than the traditional software metrics.