A must-alias (or “definite-alias”) analysis computes sets of expressions that are guaranteed to be aliased at a given pro- gram point. The analysis has been shown to have significant practical impact, and it is actively used in popular research frameworks and commercial tools. We present a custom data structure that speeds up must-alias analysis by nearly two orders of magnitude (while computing identical results). The data structure achieves efficiency by encoding multiple alias sets in a single linked structure, and compactly representing the aliasing relations of arbitrarily long expressions. We ex- plore the data structure’s performance in both an imperative and a declarative setting and contrast it extensively with prior techniques. With our approach, must-alias analysis can be performed efficiently, over large Java benchmarks, in under half a minute, making the analysis cost acceptable for most practical uses.

Static analyses for reflection and other dynamic language features have recently increased in number and advanced in sophistication. Most such analyses rely on a whole-program model of the flow of strings, through the stack and heap. We show that this global modeling of strings remains a major bottleneck of static analyses and propose a compact encoding, in order to battle unnecessary complexity. In our encoding, strings are maximally merged if they can never serve to differentiate class members in reflection operations. We formulate the problem as an instance of graph coloring and propose a fast polynomial-time algorithm that exploits the unique features of the setting (esp. large cliques, leading to hundreds of colors for realistic programs). The encoding is applied to two different frameworks for string-guided Java reflection analysis from past literature and leads to significant optimization (e.g., a ~2x reduction in the number of string-flow inferences), for a whole-program points-to analysis that uses strings.

We present a defensive may-point-to analysis approach, which offers soundness even in the presence of arbitrary opaque code: all non-empty points-to sets computed are guaranteed to be over-approximations of the sets of values arising at run time. A key design tenet of the analysis is laziness: the analysis computes points-to relationships only for variables or objects that are guaranteed to never escape into opaque code. This means that the analysis misses some valid inferences, yet it also never wastes work to compute sets of values that are not "complete", i.e., that may be missing elements due to opaque code. Laziness enables great efficiency, allowing a highly precise points-to analysis (such as a 5-call-site-sensitive, flow-sensitive analysis). Despite its conservative nature, our analysis yields sound, actionable results for a large subset of the program code, achieving (under worst-case assumptions) 34-74% of the program coverage of an unsound state-of-the-art analysis for real-world programs.

The dynamic proxy API is one of Java’s most widely-used dynamic features, permitting principled run-time code generation and link- ing. Dynamic proxies can implement any set of interfaces and for- ward method calls to a special object that handles them reflectively. The flexibility of dynamic proxies, however, comes at the cost of having a dynamically generated layer of bytecode that cannot be penetrated by current static analyses. In this paper, we observe that the dynamic proxy API is stylized enough to permit static analysis. We show how the semantics of dynamic proxies can be modeled in a straightforward manner as logical rules in the Doop static analysis framework. This concise set of rules enables Doop’s standard analyses to process code behind dynamic proxies. We evaluate our approach by analyzing XCorpus, a corpus of real-world Java programs: we fully handle 95% of its reported proxy creation sites. Our handling results in the analysis of significant portions of previously unreachable or incompletely- modeled code.

Context-sensitivity is the primary approach for adding more precision to a points-to analysis, while hopefully also maintaining scalability. An oft-reported problem with context-sensitive analyses, however, is that they are bi-modal: either the analysis is precise enough that it manipulates only manageable sets of data, and thus scales impressively well, or the analysis gets quickly derailed at the first sign of imprecision and becomes orders-of-magnitude more expensive than would be expected given the program's size. There is currently no approach that makes precise context-sensitive analyses (of any flavor: call-site-, object-, or type-sensitive) scale across the board at a level comparable to that of a context-insensitive analysis. To address this issue, we propose introspective analysis: a technique for uniformly scaling context-sensitive analysis by eliminating its performance-detrimental behavior, at a small precision expense. Introspective analysis consists of a common adaptivity pattern: first perform a context-insensitive analysis, then use the results to selectively refine (i.e., analyze context-sensitively) program elements that will not cause explosion in the running time or space. The technical challenge is to appropriately identify such program elements. We show that a simple but principled approach can be remarkably effective, achieving scalability (often with dramatic speedup) for benchmarks previously completely out-of-reach for deep context-sensitive analyses.

We present set-based pre-analysis: a virtually universal optimization technique for flow-insensitive points-to analysis. Points-to analysis computes a static abstraction of how object values flow through a program's variables. Set-based pre-analysis relies on the observation that much of this reasoning can take place at the set level rather than the value level. Computing constraints at the set level results in significant optimization opportunities: we can rewrite the input program into a simplified form with the same essential points-to properties. This rewrite results in removing both local variables and instructions, thus simplifying the subsequent value-based points-to computation. Effectively, set-based pre-analysis puts the program in a normal form optimized for points-to analysis.

Context-sensitive points-to analysis is valuable for achieving high precision with good performance. The standard flavors of context-sensitivity are call-site-sensitivity (kCFA) and object-sensitivity. Combining both flavors of context-sensitivity increases precision but at an infeasibly high cost. We show that a selective combination of call-site- and object-sensitivity for Java points-to analysis is highly profitable. Namely, by keeping a combined context only when analyzing selected language features, we can closely approximate the precision of an analysis that keeps both contexts at all times. In terms of speed, the selective combination of both kinds of context not only vastly outperforms non-selective combinations but is also faster than a mere object-sensitive analysis. This result holds for a large array of analyses (e.g., 1-object-sensitive, 2-object-sensitive with a context-sensitive heap, type-sensitive) establishing a new set of performance/precision sweet spots.