The IFDS algorithm is pivotal in solving field-sensitive data-flow problems. However, its conventional use of access paths for field sensitivity leads to the generation of a large number of data-flow facts. This causes scalability challenges in larger programs, limiting its practical application in extensive codebases. In response, we propose a new field-sensitive technique that reinterprets the generation of access paths as a Context Free Language (CFL) for field-sensitivity and formulates it as an IDE problem. This approach significantly reduces the number of data-flow facts generated and handled during the analysis, which is a major factor in performance degradation.

To demonstrate the effectiveness of this approach, we developed a taint analysis tool, IDEDroid, in the IFDS/IDE framework. IDEDroid outperforms FlowDroid, an established IFDS-based taint analysis tool, in the analysis of 24 major Android apps while improving its precision (guaranteed theoretically). The speed improvement ranges from $2.1\times$ to $2,368.4\times$, averaging at $222.0\times$, with precision gains reaching up to $20.0%$ (in terms of false positives reduced). This performance indicates that IDEDroid is substantially more effective in detecting information-flow leaks, making it a potentially superior tool for mobile app vetting in the market.