IFDS taint analysis is inherently context- and flow-sensitive, allowing precise encoding of field sensitivity in access-path generation. However, preserving this level of precision in practice is difficult, leading to over-tainting - marking more data facts as tainted than necessary. The root cause is the undecidability of solving two context-free language reachability (CFL-reachability) problems along the same dataflow path, which forces k-limiting as an over-approximation of field sensitivity. Consequently, spurious access paths are introduced, increasing analysis time, memory usage, and false positives, especially in large-scale applications. To address this challenge, we present TnFix, a CFL-reachability-based technique for mitigating over-tainting in IFDS taint analysis. The key insight is that the field sequence of any candidate tainted access path can be checked by a deterministic finite automaton (DFA) that accepts feasible sequences of field accesses. TnFix builds these DFAs by first solving a lightweight field-sensitive CFL-reachability problem to construct a Field Points-to Graph (FPG) that integrates data flows from taint sources and library summaries, and then converting the FPG into per-object DFAs. During taint analysis, TnFix queries these DFAs to prune access paths whose field sequences are rejected, eliminating the spurious paths introduced by k-limiting and improving precision without sacrificing scalability. In a comparative evaluation against FlowDroid on a set of 36 widely used Android apps for taint analysis, TnFix successfully analyzes 7 apps that FlowDroid cannot complete within a three-hour time budget. For the remaining 29 apps, it improves analysis speed by an average of 2.5× and reduces false positives by an average of 12.2%. TnFix thus establishes the first CFL-based optimization framework for reducing over-tainting in IFDS taint analysis, delivering substantial gains in both efficiency and precision for practical use.

Taint analysis, widely used for bug and vulnerability detection, is typically formulated as a flow- and context-sensitive IFDS analysis. To achieve field sensitivity, IFDS models heap locations as k-limited access paths but suffers from cubic time and quadratic space complexity, leading to prohibitive costs under realistic memory budgets and frequent out-of-memory failures or timeouts. Existing improvements target scalability or precision under abundant memory but remain fragile under constrained resources. We present ReFine, an iterative restart-and-refinement framework that enables scalable IFDS taint analysis across diverse memory budgets. When memory is exhausted, ReFine reuses partial results from terminated runs as sound under-approximations to guide subsequent iterations. Each restart occurs at a partial-analysis point, where results are abstracted and refined by leveraging that taint propagation is monotonic under field extension—allowing longer access paths to be safely summarized by their prefixes. We formalize this process as a fixpoint computation over product semilattices and prove soundness, correctness, and termination. Evaluated on 31 real-world Android apps against a state-of-the-art IFDS taint analysis, ReFine wraps it in a restart-and-refinement framework, analyzing 5.0x more apps under 16 GB and 2.4x more under 800 GB, with up to 52.5x speedup. By turning partial analyses into progressive refinement, ReFine delivers sound, precise, and highly scalable IFDS taint analysis across diverse memory budgets.

Delta debugging assumes search space monotonicity: if a program causes a failure, any supersets of that program will also induce the same failure, permitting the exclusion of subsets of non-failure-inducing programs. However, this assumption does not always hold in practice. This paper introduces Probabilistic Monotonicity Assessment (PMA), enhancing the efficiency of ddmin-style algorithms without sacrificing effectiveness. PMA dynamically models and assesses the search space’s monotonicity based on prior tests tried during the debugging process and uses a confidence function to quantify monotonicity, thereby enabling the probabilistic exclusion of subsets of non-failure-inducing programs. Our approach significantly reduces redundant tests that would otherwise be performed, without compromising the quality of the reduction.

In this paper, we introduce DebloaterX, a new approach for automatically identifying context-independent objects to debloat contexts in object-sensitive pointer analysis ( k obj). Object sensitivity achieves high precision, but its context construction mechanism combines objects with their contexts indiscriminately. This leads to a combinatorial explosion of contexts in large programs, resulting in inefficiency. Previous research has proposed a context-debloating approach that inhibits a pre-selected set of context-independent objects from forming new contexts, improving the efficiency of k obj. However, this earlier context-debloating approach under-approximates the set of context-independent objects identified, limiting performance speedups. We introduce a novel context-debloating pre-analysis approach that identifies objects as context-dependent only when they are potentially precision-critical to k obj based on three general container-usage patterns. Our research finds that objects containing no fields of ”abstract” (i.e., open) types can be analyzed context-insensitively with negligible precision loss in real-world applications. We provide clear rules and efficient algorithms to recognize these patterns, selecting more context-independent objects for better debloating. We have implemented DebloaterX in the Qilin framework and will release it as an open-source tool. Our experimental results on 12 standard Java benchmarks and real-world programs show that DebloaterX selects 92.4% of objects to be context-independent on average, enabling k obj to run significantly faster (an average of 19.3x when k = 2 and 150.2x when k = 3) and scale up to 8 more programs when k = 3, with only a negligible loss of precision (less than 0.2%). Compared to state-of-the-art alternative pre-analyses in accelerating k obj, DebloaterX outperforms Zipper significantly in both precision and efficiency and outperforms Conch (the earlier context-debloating approach) in efficiency substantially while achieving nearly the same precision.

A bipolar logarithmic intermediate-frequency (IF) amplifier with received signal strength indicator (RSSI) circuit for ASK Receiver is presented. The amplifier realizes a piecewise approximation to an exact logarithmic response. In the demodulating Log Amplifiers, a special architecture of RSSI is proposed. There are five stages in the Log amplifiers. Each consists of a limiter amplifier and a gm cell. A 90dB input dynamic range within ±1dB linearity is achieved.