We present a new technique for automatically synthesizing replacement classes. The technique starts with an original class O and a potential replacement class R, then uses R to synthesize a new class that implements the same interface and provides the same functionality as O. Critically, our technique works with a synthe- sized inter-class equivalence predicate between the states of O and R. It uses this predicate to ensure that original and synthesized methods leave corresponding O and R objects in equivalent states. The predicate therefore enables the technique to synthesize individual replacement methods in isolation while still obtain- ing a replacement class that leaves the original and replacement objects in equivalent states after arbitrarily long method invocation sequences. We have implemented the technique as part of a tool, named Mask, and evaluated it using open-source Java classes. The results highlight the effectiveness of Mask in synthesizing replacement classes.

We present a new technique and system, DIODE, for auto- matically generating inputs that trigger overflows at memory allocation sites. DIODE is designed to identify relevant sanity checks that inputs must satisfy to trigger overflows at target memory allocation sites, then generate inputs that satisfy these sanity checks to successfully trigger the overflow. DIODE works with off-the-shelf, production x86 binaries. Our results show that, for our benchmark set of applications, and for every target memory allocation site exercised by our seed inputs (which the applications process correctly with no overflows), either 1) DIODE is able to generate an input that triggers an overflow at that site or 2) there is no input that would trigger an overflow for the observed target expression at that site.

We present a system, SIFT, for generating input filters that nullify integer overflow errors associated with critical program sites such as memory allocation or block copy sites. SIFT uses a static pro- gram analysis to generate filters that discard inputs that may trigger integer overflow errors in the computations of the sizes of allocated memory blocks or the number of copied bytes in block copy operations. Unlike all previous techniques of which we are aware, SIFT is sound -- if an input passes the filter, it will not trigger an integer overflow error at any analyzed site. Our results show that SIFT successfully analyzes (and therefore generates sound input filters for) 56 out of 58 memory allocation and block memory copy sites in analyzed input processing modules from five applications (VLC, Dillo, Swfdec, Swftools, and GIMP). These nullified errors include six known integer overflow vulnerabilities. Our results also show that applying these filters to 62895 real-world inputs produces no false positives. The analysis and filter generation times are all less than a second.

Approximate computations (for example, video, audio, and image processing, machine learning, and many scientific computations) have the freedom to generate a range of acceptable results. Approximate program transformations (for example, task skipping and loop perforation) exploit this freedom to produce computations that can execute at a variety of points in an underlying accuracy versus performance trade-off space. One potential concern is that these transformations may change the semantics of the program and therefore cause the program to crash, perform an illegal operation, or otherwise violate its integrity.

We present QuickStep, a novel system for parallelizing sequential programs. Unlike standard parallelizing compilers (which are designed to preserve the semantics of the original sequential computation), QuickStep is instead designed to generate (potentially nondeterministic) parallel programs that produce acceptably accurate results acceptably often. The freedom to generate parallel programs whose output may differ (within statistical accuracy bounds) from the output of the sequential program enables a dramatic simplification of the compiler, a dramatic increase in the range of applications that it can parallelize, and a significant expansion in the range of parallel programs that it can legally generate. Results from our benchmark set of applications show that QuickStep can automatically generate acceptably accurate and efficient parallel programs---the automatically generated parallel versions of five of our six benchmark applications run between 5.0 and 7.8 times faster on eight cores than the original sequential versions. These applications and parallelizations contain features (such as the use of modern object-oriented programming constructs or desirable parallelizations with infrequent but acceptable data races) that place them inherently beyond the reach of standard approaches.

Approximate program transformations such as skipping tasks [29, 30], loop perforation [21, 22, 35], reduction sampling [38], multiple selectable implementations [3, 4, 16, 38], dynamic knobs [16], synchronization elimination [20, 32], approximate function memoization [11],and approximate data types [34] produce programs that can execute at a variety of points in an underlying performance versus accuracy tradeoff space. These transformed programs have the ability to trade accuracy of their results for increased performance by dynamically and nondeterministically modifying variables that control their execution.

We present a new technique for verifying commutativity conditions, which are logical formulas that characterize when operations commute. Because our technique reasons with the abstract state of verified linked data structure implementations, it can verify commuting operations that produce semantically equivalent (but not necessarily identical) data structure states in different execution orders. We have used this technique to verify sound and complete commutativity conditions for all pairs of operations on a collection of linked data structure implementations, including data structures that export a set interface (ListSet and HashSet) as well as data structures that export a map interface (AssociationList, HashTable, and ArrayList). This effort involved the specification and verification of 765 commutativity conditions.