Mihir Parmar, Nisarg Patel, Neeraj Varshney, Mutsumi Nakamura, Man Luo, Santosh Mashetty, Arindam Mitra, Chitta Baral. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Nisarg Patel, Mohith Kulkarni, Mihir Parmar, Aashna Budhiraja, Mutsumi Nakamura, Neeraj Varshney, Chitta Baral. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.

Nemika Tyagi, Mihir Parmar, Mohith Kulkarni, Aswin Rrv, Nisarg Patel, Mutsumi Nakamura, Arindam Mitra, Chitta Baral. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.

Binary reverse engineering is an arduous and tedious task performed by skilled and expensive human analysts. Information about the source code is irrevocably lost in the compilation process. While modern decompilers attempt to generate C-style source code from a binary, they cannot recover lost variable names. Prior works have explored machine learning techniques for predicting variable names in decompiled code. However, the state-of-the-art systems, DIRE and DIRTY, generalize poorly to functions in the testing set that are not included in the training set—31.8% for DIRE on DIRTY’s data set and 36.9% for DIRTY on DIRTY’s data set.In this paper, we present VarBERT, a Bidirectional Encoder Representations from Transformers (BERT) to predict meaningful variable names in decompilation output. An advantage of VarBERT is that we can pre-train on human source code and then fine-tune the model to the task of predicting variable names. We also create a new data set VarCorpus, which significantly expands the size and variety of the data set. Our evaluation of VarBERT on VarCorpus, demonstrates a significant improvement in predicting the developer’s original variable names for O2 optimized binaries achieving accuracies of 54.43% for IDA and 54.49% for Ghidra. VarBERT is strictly better than state-of-the-art techniques: On a subset of VarCorpus, VarBERT could predict the developer’s original variable names 50.70% of the time, while DIRE and DIRTY predicted original variable names 35.94% and 38.00% of the time, respectively.

In this paper we show that the language of declarative logic programming (DLP) with answer sets and its extensions can be used to specify database evolution due to updates and active rules, and to verify correctness of active rules with respect to a specification described using temporal logic and aggregate operators. We classify the specification of active rules into four kind of constraints which can be expressed using a particular extension of DLP called Smodels. Smodels allows us to specify the evolution, to specify the constraints, and to enumerate all possible initial database states and initial updates. Together, these can be used to analyze all possible evolution paths of an active database system to verify if they satisfy a set of given constraints.

This paper presents a rationale for providing hardware supported functions of more than two variables for processing incomplete knowledge and fuzzy knowledge. The result is in contrast to Kolmogorov's (1957) theorem in the numerical (nonfuzzy) case.