Dynamic binary translation (DBT) is widely used in system virtualization and many other important applications. To achieve a higher translation quality, a learning-based approach has been recently proposed to automatically learn semantically-equivalent translation rules. Because translation rules directly impact the quality and performance of the translated host codes, one of the key issues is to collect as many translation rules as possible through minimal training data set. The collected translation rules should also cover (i.e. apply to) as many guest binary instructions or code sequences as possible at the runtime. For those guest binary instructions that are not covered by the learned rules, emulation has to be used, which will incur additional runtime overhead. Prior learning-based DBT systems only achieve an average of about 69% dynamic code coverage for SPEC CINT 2006.In this paper, we propose a novel parameterization approach to take advantage of the regularity and the well-structured format in most modern ISAs. It allows us to extend the learned translation rules to include instructions or instruction sequences of similar structures or characteristics that are not covered in the training set. More translation rules can thus be harvested from the same training set. Experimental results on QEMU 4.1 show that using such a parameterization approach we can expand the learned 2,724 rules to 86,423 applicable rules for SPEC CINT 2006. Its code coverage can also be expanded from about 69.7% to about 95.5% with a 24% performance improvement compared to enhanced learning-based approach.

In order to help students learning to develop computer programs, several computing education researchers have analyzed the compiler error messages generated by novices' attempts to compile their programs. The goal is to help students diagnose the errors they make through the messages generated by the compiler. This paper builds on that previous work by applying a technique based on a heuristic well-known to programmers - fix the first error and ignore the rest - to the analysis of over 21 million compiler error messages from the Blackbox dataset. We find that the ranks and frequencies obtained by considering all error messages are generally consistent with previously published lists, but when we consider first messages only, these ranks and frequencies are different. These differences could have important implications for teaching, and can inform tool design and future research efforts.