Michael Coblenz

Shun Kashiwa, Ayla Kurdak, Savitha Ravi et al.

The quality of scientific code is a critical concern for the research community. Poorly written code can result in irreproducible results, incorrect findings, and slower scientific progress. In this study, we evaluate scientific code quality across three dimensions: reproducibility, readability, and reusability. We curated a corpus of 518 code repositories by analyzing Code Availability statements from all 1239 Nature publications in 2024. To assess code quality, we employed multiple methods, including manual attempts to reproduce Jupyter notebooks, documentation reviews, and analyses of code clones and mutation patterns. Our results reveal major challenges in scientific code quality. Of the 19 notebooks we attempted to execute, only two were reproducible, primarily due to missing data files and dependency issues. Code duplication was also common, with 326 clone classes of at least 10 lines and three instances found among 637 of the 1510 notebooks in our corpus. These duplications frequently involved tasks such as visualization, data processing, and statistical analysis. Moreover, our mutation analysis showed that scientific notebooks often exhibit tangled state changes, complicating comprehension and reasoning. The prevalence of these issues -- unreproducible code, widespread duplication, and tangled state management -- underscores the need for improved tools and abstractions to help science build reproducible, readable and reusable software.

Timothy Mou, Michael Coblenz, Jonathan Aldrich

Smart contracts are programs that are executed on a blockhain. They have been used for applications in voting, decentralized finance, and supply chain management. However, vulnerabilities in smart contracts have been abused by hackers, leading to financial losses. Understanding state machine protocols in smart contracts has been identified as important to catching common bugs, improving documentation, and optimizing smart contracts. We analyze Solidity smart contracts deployed on the Ethereum blockchain and study the prevalence of protocols and protocol-based bugs, as well as opportunities for gas optimizations.

Michael Coblenz

Current approaches to making programming languages and reasoning assistants more effective for people focus on leveraging feedback from users and on evaluating the success of particular techniques. These approaches, although helpful, may not result in systems that are as usable as possible, and may not lead to general design principles. This paper advocates for leveraging theories from cognitive science, focusing on cognitive load theory, to design more effective programming languages and reasoning assistants. Development of these theories may enable designers to create more effective programming languages and reasoning assistants at lower cost.

Michael Coblenz, Michelle Mazurek, Michael Hicks

Rust is a general-purpose programming language that is both type- and memory-safe. Rust does not use a garbage collector, but rather achieves these properties through a sophisticated, but complex, type system. Doing so makes Rust very efficient, but makes Rust relatively hard to learn and use. We designed Bronze, an optional, library-based garbage collector for Rust. To see whether Bronze could make Rust more usable, we conducted a randomized controlled trial with volunteers from a 633-person class, collecting data from 428 students in total. We found that for a task that required managing complex aliasing, Bronze users were more likely to complete the task in the time available, and those who did so required only about a third as much time (4 hours vs. 12 hours). We found no significant difference in total time, even though Bronze users re-did the task without Bronze afterward. Surveys indicated that ownership, borrowing, and lifetimes were primary causes of the challenges that users faced when using Rust.

Michael Coblenz, Jonathan Aldrich, Joshua Sunshine et al.

Some blockchain programs (smart contracts) have included serious security vulnerabilities. Obsidian is a new typestate-oriented programming language that uses a strong type system to rule out some of these vulnerabilities. Although Obsidian was designed to promote usability to make it as easy as possible to write programs, strong type systems can cause a language to be difficult to use. In particular, ownership, typestate, and assets, which Obsidian uses to provide safety guarantees, have not seen broad adoption together in popular languages and result in significant usability challenges. We performed an empirical study with 20 participants comparing Obsidian to Solidity, which is the language most commonly used for writing smart contracts today. We observed that Obsidian participants were able to successfully complete more of the programming tasks than the Solidity participants. We also found that the Solidity participants commonly inserted asset-related bugs, which Obsidian detects at compile time.

Michael Coblenz, Gauri Kambhatla, Paulette Koronkevich et al.

Programming language design requires making many usability-related design decisions. However, existing HCI methods can be impractical to apply to programming languages: they have high iteration costs, programmers require significant learning time, and user performance has high variance. To address these problems, we adapted both formative and summative HCI methods to make them more suitable for programming language design. We integrated these methods into a new process, PLIERS, for designing programming languages in a user-centered way. We evaluated PLIERS by using it to design two new programming languages. Glacier extends Java to enable programmers to express immutability properties effectively and easily. Obsidian is a language for blockchains that includes verification of critical safety properties. Summative usability studies showed that programmers were able to program effectively in both languages after short training periods.

Michael Coblenz, Reed Oei, Tyler Etzel et al.

Blockchain platforms are coming into broad use for processing critical transactions among participants who have not established mutual trust. Many blockchains are programmable, supporting smart contracts, which maintain persistent state and support transactions that transform the state. Unfortunately, bugs in many smart contracts have been exploited by hackers. Obsidian is a novel programming language with a type system that enables static detection of bugs that are common in smart contracts today. Obsidian is based on a core calculus, Silica, for which we proved type soundness. Obsidian uses typestate to detect improper state manipulation and uses linear types to detect abuse of assets. We describe two case studies that evaluate Obsidian's applicability to the domains of parametric insurance and supply chain management, finding that Obsidian's type system facilitates reasoning about high-level states and ownership of resources. We compared our Obsidian implementation to a Solidity implementation, observing that the Solidity implementation requires much boilerplate checking and tracking of state, whereas Obsidian does this work statically.