Shaiful Chowdhury — Page 2

arXiv2024

Versioned Analysis of Software Quality Indicators and Self-admitted Technical Debt in Ethereum Smart Contracts with Ethstractor

The rise of decentralized applications (dApps) has made smart contracts imperative components of blockchain technology. As many smart contracts process financial transactions, their security is paramount. Moreover, the immutability of blockchains makes vulnerabilities in smart contracts particularly challenging because it requires deploying a new version of the contract at a different address, incurring substantial fees paid in Ether. This paper proposes Ethstractor, the first smart contract collection tool for gathering a dataset of versioned smart contracts. The collected dataset is then used to evaluate the reliability of code metrics as indicators of vulnerabilities in smart contracts. Our findings indicate that code metrics are ineffective in signalling the presence of vulnerabilities. Furthermore, we investigate whether vulnerabilities in newer versions of smart contracts are mitigated and identify that the number of vulnerabilities remains consistent over time. Finally, we examine the removal of self-admitted technical debt in contracts and uncover that most of the introduced debt has never been subsequently removed.

cs.DC↗

arXiv2019

Syntax and Stack Overflow: A methodology for extracting a corpus of syntax errors and fixes

One problem when studying how to find and fix syntax errors is how to get natural and representative examples of syntax errors. Most syntax error datasets are not free, open, and public, or they are extracted from novice programmers and do not represent syntax errors that the general population of developers would make. Programmers of all skill levels post questions and answers to Stack Overflow which may contain snippets of source code along with corresponding text and tags. Many snippets do not parse, thus they are ripe for forming a corpus of syntax errors and corrections. Our primary contribution is an approach for extracting natural syntax errors and their corresponding human made fixes to help syntax error research. A Python abstract syntax tree parser is used to determine preliminary errors and corrections on code blocks extracted from the SOTorrent data set. We further analyzed our code by executing the corrections in a Python interpreter. We applied our methodology to produce a public data set of 62,965 Python Stack Overflow code snippets with corresponding tags, errors, and stack traces. We found that errors made by Stack Overflow users do not match errors made by student developers or random mutations, implying there is a serious representativeness risk within the field. Finally we share our dataset openly so that future researchers can re-use and extend our syntax errors and fixes.

cs.SE↗