ﻻ يوجد ملخص باللغة العربية
Recently, the automated translation of source code from one programming language to another by using automatic approaches inspired by Neural Machine Translation (NMT) methods for natural languages has come under study. However, such approaches suffer from the same problem as previous NMT approaches on natural languages, viz. the lack of an ability to estimate and evaluate the quality of the translations; and consequently ascribe some measure of interpretability to the models choices. In this paper, we attempt to estimate the quality of source code translations built on top of the TransCoder model. We consider the code translation task as an analog of machine translation (MT) for natural languages, with some added caveats. We present our main motivation from a user study built around code translation; and present a technique that correlates the confidences generated by that model to lint errors in the translated code. We conclude with some observations on these correlations, and some ideas for future work.
Context: Decentralized applications on blockchain platforms are realized through smart contracts. However, participants who lack programming knowledge often have difficulties reading the smart contract source codes, which leads to potential security
In this paper, our aim is to propose a model for code abstraction, based on abstract interpretation, allowing us to improve the precision of a recently proposed static analysis by abstract interpretation of dynamic languages. The problem we tackle he
Program representation learning is a fundamental task in software engineering applications. With the availability of big code and the development of deep learning techniques, various program representation learning models have been proposed to unders
Automatic program repair (APR) is crucial to improve software reliability. Recently, neural machine translation (NMT) techniques have been used to fix software bugs automatically. While promising, these approaches have two major limitations. Their se
Infrastructure-as-code (IaC) is a practice to implement continuous deployment by allowing management and provisioning of infrastructure through the definition of machine-readable files and automation around them, rather than physical hardware configu