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Numerous trace retrieval algorithms incorporate the standard tf-idf (term frequency, inverse document frequency) technique to weight various terms. In this paper we address Grand Challenge C-GC1 by comparing the effectiveness of computing idf based only on the local terms in the query, versus computing it based on general term usage as documented in the American National Corpus. We also address Grand Challenges L-GC1 and L-GC2 by setting ourselves the additional task of designing and conducting the experiments using the alpha-release of TraceLab. TraceLab is an experimental workbench which allows researchers to graphically model and execute a traceability experiment as a workflow of components. Results of the experiment show that the local idf approach exceeds or matches the global approach in all of the cases studied.

Software complexity is often hypothesized to be the enemy of software security. We performed statistical analysis on nine code complexity metrics from the JavaScript Engine in the Mozilla application framework to investigate if this hypothesis is true. Our initial results show that the nine complexity measures have weak correlation (ρ=0.30 at best) with security problems for Mozilla JavaScript Engine. The study should be replicated on more products with design and code-level metrics. It may be necessary to create new complexity metrics to embody the type of complexity that leads to security problems.

Complexity is often hypothesized to be the enemy of software security. If this hypothesis is true, complexity metrics may be used to predict the locale of security problems and can be used to prioritize inspection and testing efforts. We performed statistical analysis on nine complexity metrics from the JavaScript Engine in the Mozilla application framework to find differences in code metrics between vulnerable and nonvulnerable code and to predict vulnerabilities. Our initial results show that complexity metrics can predict vulnerabilities at a low false positive rate, but at a high false negative rate.

An increasing number of cyber attacks are occurring at the application layer when attackers use malicious input. These input validation vulnerabilities can be exploited by (among others) SQL injection, cross site scripting, and buffer overflow attacks. Statement coverage and similar test adequacy metrics have historically been used to assess the level of functional and unit testing which has been performed on an application. However, these currently-available metrics do not highlight how well the system protects itself through validation. In this paper, we propose two SQL injection input validation testing adequacy metrics: target statement coverage and input variable coverage. A test suite which satisfies both adequacy criteria can be leveraged as a solid foundation for input validation scanning with a blacklist. To determine whether it is feasible to calculate values for our two metrics, we perform a case study on a web healthcare application and discuss some issues in implementation we have encountered. We find that the web healthcare application scored 96.7% target statement coverage and 98.5% input variable coverage.