Edsger Dijkstra argues against "natural language programming," believing it a foolish endeavor. He contends that natural language's inherent ambiguity and imprecision make it unsuitable for expressing the rigorous logic required in programming. Instead of striving for superficial readability through natural language, Dijkstra advocates for focusing on developing formal notations and abstractions that are clear, concise, and verifiable, even if they appear less "natural" initially. He emphasizes that programming requires a level of precision and unambiguity that natural language simply cannot provide, and attempting to bridge this gap will ultimately lead to more confusion and less reliable software.
Deduce is a proof checker designed specifically for educational settings. It aims to bridge the gap between informal mathematical reasoning and formal proof construction by providing a simple, accessible interface and a focused set of logical connectives. Its primary goal is to teach the core concepts of formal logic and proof techniques without overwhelming users with complex syntax or advanced features. The system supports natural deduction style proofs and offers immediate feedback, guiding students through the process of building valid arguments step-by-step. Deduce prioritizes clarity and ease of use to make learning formal logic more engaging and less daunting.
Hacker News users discussed the educational value of the Deduce proof checker. Several commenters appreciated its simplicity and accessibility compared to other systems like Coq, finding its focus on propositional and first-order logic suitable for introductory logic courses. Some suggested potential improvements, such as adding support for natural deduction and incorporating a more interactive tutorial. Others debated the pedagogical merits of different proof styles and the balance between automated assistance and requiring students to fill in proof steps themselves. The overall sentiment was positive, with many seeing Deduce as a promising tool for teaching logic.
This 1986 paper explores representing the complex British Nationality Act 1981 as a Prolog program. It demonstrates how Prolog's declarative nature and built-in inference mechanisms can effectively encode the Act's intricate rules regarding citizenship acquisition and loss. The authors translate legal definitions of British citizenship, descent, and residency into Prolog clauses, showcasing the potential of logic programming to represent and reason with legal statutes. While acknowledging the limitations of this initial attempt, such as simplifying certain aspects of the Act and handling time-dependent clauses, the paper highlights the potential of using Prolog for legal expert systems and automated legal reasoning. It ultimately serves as an early exploration of applying computational logic to the domain of law.
Hacker News users discussed the ingenuity of representing the British Nationality Act as a Prolog program, highlighting the elegance of Prolog for handling complex logic and legal rules. Some expressed nostalgia for the era's focus on symbolic AI and rule-based systems. Others debated the practicality and maintainability of such an approach for real-world legal applications, citing the potential difficulty of updating and debugging the code as laws change. The discussion also touched on the broader implications of encoding law in a computationally interpretable format, considering the benefits for automated legal reasoning and the potential risks of bias and misinterpretation. Some users shared their own experiences with Prolog and other logic programming languages, and pondered the reasons for their decline in popularity despite their inherent strengths for certain problem domains.
This 1987 paper by Dybvig explores three distinct implementation models for Scheme: compilation to machine code, abstract machine interpretation, and direct interpretation of source code. It argues that while compilation offers the best performance for finished programs, the flexibility and debugging capabilities of interpreters are crucial for interactive development environments. The paper details the trade-offs between these models, emphasizing the advantages of a mixed approach that leverages both compilation and interpretation techniques. It concludes that an ideal Scheme system would utilize compilation for optimized execution and interpretation for interactive use, debugging, and dynamic code loading, hinting at a system where the boundaries between compiled and interpreted code are blurred.
HN commenters discuss the historical significance of the paper in establishing Scheme's minimalist design and portability. They highlight the cleverness of the three implementations, particularly the threaded code interpreter, and its influence on later languages like Lua. Some note the paper's accessibility and clarity, even for those unfamiliar with Scheme, while others reminisce about using the techniques described. A few comments delve into technical details like register allocation and garbage collection, comparing the approaches to modern techniques. The overall sentiment is one of appreciation for the paper's contribution to computer science and programming language design.
This paper explores using first-order logic (FOL) to detect logical fallacies in natural language arguments. The authors propose a novel approach that translates natural language arguments into FOL representations, leveraging semantic role labeling and a defined set of predicates to capture argument structure. This structured representation allows for the application of automated theorem provers to evaluate the validity of the arguments, thus identifying potential fallacies. The research demonstrates improved performance compared to existing methods, particularly in identifying fallacies related to invalid argument structure, while acknowledging limitations in handling complex linguistic phenomena and the need for further refinement in the translation process. The proposed system provides a promising foundation for automated fallacy detection and contributes to the broader field of argument mining.
Hacker News users discussed the potential and limitations of using first-order logic (FOL) for fallacy detection as described in the linked paper. Some praised the approach for its rigor and potential to improve reasoning in AI, while also acknowledging the inherent difficulty of translating natural language to FOL perfectly. Others questioned the practical applicability, citing the complexity and ambiguity of natural language as major obstacles, and suggesting that statistical/probabilistic methods might be more robust. The difficulty of scoping the domain knowledge necessary for FOL translation was also brought up, with some pointing out the need for extensive, context-specific knowledge bases. Finally, several commenters highlighted the limitations of focusing solely on logical fallacies for detecting flawed reasoning, suggesting that other rhetorical tactics and nuances should also be considered.
This paper details the formal verification of a garbage collector for a substantial subset of OCaml, including higher-order functions, algebraic data types, and mutable references. The collector, implemented and verified using the Coq proof assistant, employs a hybrid approach combining mark-and-sweep with Cheney's copying algorithm for improved performance. A key achievement is the proof of correctness showing that the garbage collector preserves the semantics of the original OCaml program, ensuring no unintended behavior alterations due to memory management. This verification increases confidence in the collector's reliability and serves as a significant step towards a fully verified implementation of OCaml.
Hacker News users discuss a mechanically verified garbage collector for OCaml, focusing on the practical implications of such verification. Several commenters express skepticism about the real-world performance impact, questioning whether the verification translates to noticeable improvements in speed or reliability for average users. Some highlight the trade-offs between provable correctness and potential performance limitations. Others note the significance of the work for critical systems where guaranteed safety and predictable behavior are paramount, even at the cost of some performance. The discussion also touches on the complexity of garbage collection and the challenges in achieving both efficiency and correctness. Some commenters raise concerns about the applicability of the specific approach to other languages or garbage collection algorithms.
The blog post details a formal verification of the standard long division algorithm using the Dafny programming language and its built-in Hoare logic capabilities. It walks through the challenges of representing and reasoning about the algorithm within this formal system, including defining loop invariants and handling edge cases like division by zero. The core difficulty lies in proving that the quotient and remainder produced by the algorithm are indeed correct according to the mathematical definition of division. The author meticulously constructs the necessary pre- and post-conditions, and elaborates on the specific insights and techniques required to guide the verifier to a successful proof. Ultimately, the post demonstrates the power of formal methods to rigorously verify even relatively simple, yet subtly complex, algorithms.
Hacker News users discussed the application of Hoare logic to verify long division, with several expressing appreciation for the clear explanation and visualization of the algorithm. Some commenters debated the practical benefits of formal verification for such a well-established algorithm, questioning the likelihood of uncovering unknown bugs. Others highlighted the educational value of the exercise, emphasizing the importance of understanding foundational algorithms. A few users delved into the specifics of the chosen proof method and its implications. One commenter suggested exploring alternative verification approaches, while another pointed out the potential for applying similar techniques to other arithmetic operations.
Hillel Wayne's post dissects the concept of "nondeterminism" in computer science, arguing that it's often used ambiguously and encompasses five distinct meanings. These are: 1) Implementation-defined behavior, where the language standard allows for varied outcomes. 2) Unspecified behavior, similar to implementation-defined but offering even less predictability. 3) Error/undefined behavior, where anything could happen, often leading to crashes. 4) Heisenbugs, which are bugs whose behavior changes under observation (e.g., debugging). 5) True nondeterminism, exemplified by hardware randomness or concurrency races. The post emphasizes that these are fundamentally different concepts with distinct implications for programmers, and understanding these nuances is crucial for writing robust and predictable software.
Hacker News users discussed various aspects of nondeterminism in the context of Hillel Wayne's article. Several commenters highlighted the distinction between predictable and unpredictable nondeterminism, with some arguing the author's categorization conflated the two. The importance of distinguishing between sources of nondeterminism, such as hardware, OS scheduling, and program logic, was emphasized. One commenter pointed out the difficulty in achieving true determinism even with seemingly simple programs due to factors like garbage collection and just-in-time compilation. The practical challenges of debugging nondeterministic systems were also mentioned, along with the value of tools that can help reproduce and analyze nondeterministic behavior. A few comments delved into specific types of nondeterminism, like data races and the nuances of concurrency, while others questioned the usefulness of the proposed categorization in practice.
Dusa is a logic programming language based on finite-choice logic, designed for declarative problem solving and knowledge representation. It emphasizes simplicity and approachability, with a Python-inspired syntax and built-in support for common data structures like lists and dictionaries. Dusa programs define relationships between facts and rules, allowing users to describe problems and let the system find solutions. Its core features include backtracking search, constraint satisfaction, and a type system based on logical propositions. Dusa aims to be both a practical tool for everyday programming tasks and a platform for exploring advanced logic programming concepts.
Hacker News users discussed Dusa's novel approach to programming with finite-choice logic, expressing interest in its potential for formal verification and constraint solving. Some questioned its practicality and performance compared to established Prolog implementations, while others highlighted the benefits of its clear semantics and type system. Several commenters drew parallels to miniKanren, another logic programming language, and discussed the trade-offs between Dusa's finite-domain focus and the more general approach of Prolog. The static typing and potential for compile-time optimization were seen as significant advantages. There was also a discussion about the suitability of Dusa for specific domains like game AI and puzzle solving. Some expressed skepticism about the claim of "blazing fast performance," desiring benchmarks to validate it. Overall, the comments reflected a mixture of curiosity, cautious optimism, and a desire for more information, particularly regarding real-world applications and performance comparisons.
This paper introduces Crusade, a formally verified translation from a subset of C to safe Rust. Crusade targets a memory-safe dialect of C, excluding features like arbitrary pointer arithmetic and casts. It leverages the Coq proof assistant to formally verify the translation's correctness, ensuring that the generated Rust code behaves identically to the original C, modulo non-determinism inherent in C. This rigorous approach aims to facilitate safe integration of legacy C code into Rust projects without sacrificing confidence in memory safety, a critical aspect of modern systems programming. The translation handles a substantial subset of C, including structs, unions, and functions, and demonstrates its practical applicability by successfully converting real-world C libraries.
HN commenters discuss the challenges and nuances of formally verifying the C to Rust transpiler, Cracked. Some express skepticism about the practicality of fully verifying such a complex tool, citing the potential for errors in the formal proofs themselves and the inherent difficulty of capturing all undefined C behavior. Others question the performance impact of the generated Rust code. However, many commend the project's ambition and see it as a significant step towards safer systems programming. The discussion also touches upon the trade-offs between a fully verified transpiler and a more pragmatic approach focusing on common C patterns, with some suggesting that prioritizing practical safety improvements could be more beneficial in the short term. There's also interest in the project's handling of concurrency and the potential for integrating Cracked with existing Rust tooling.
Rishi Mehta reflects on the key contributions and learnings from AlphaProof, his AI research project focused on automated theorem proving. He highlights the successes of AlphaProof in tackling challenging mathematical problems, particularly in abstract algebra and group theory, emphasizing its unique approach of combining language models with symbolic reasoning engines. The post delves into the specific techniques employed, such as the use of chain-of-thought prompting and iterative refinement, and discusses the limitations encountered. Mehta concludes by emphasizing the significant progress made in bridging the gap between natural language and formal mathematics, while acknowledging the open challenges and future directions for research in automated theorem proving.
Hacker News users discuss AlphaProof's approach to testing, questioning its reliance on property-based testing and mutation testing for catching subtle bugs. Some commenters express skepticism about the effectiveness of these techniques in real-world scenarios, arguing that they might not be as comprehensive as traditional testing methods and could lead to a false sense of security. Others suggest that AlphaProof's methodology might be better suited for specific types of problems, such as concurrency bugs, rather than general software testing. The discussion also touches upon the importance of code review and the potential limitations of automated testing tools. Some commenters found the examples provided in the original article unconvincing, while others praised AlphaProof's innovative approach and the value of exploring different testing strategies.
Summary of Comments ( 131 )
https://news.ycombinator.com/item?id=43564386
HN commenters generally agree with Dijkstra's skepticism of "natural language programming." Some highlight the ambiguity inherent in natural language as fundamentally incompatible with the precision required for programming. Others point out the success of domain-specific languages (DSLs) as a middle ground, offering a more human-readable syntax without sacrificing clarity. One commenter suggests Dijkstra's critique is more aimed at vague specifications disguised as programs rather than genuinely well-defined natural language programming. Several commenters mention the value of formal methods and mathematical notation for clear program design, echoing Dijkstra's sentiments. A few offer historical context, suggesting the "natural language programming" Dijkstra criticized likely refers to early, overly ambitious attempts, and that modern NLP advancements might warrant revisiting the concept.
The Hacker News post titled "Dijkstra On the foolishness of "natural language programming"" links to Edsger W. Dijkstra's manuscript EWD667, where he argues against using natural language for programming. The comments section features a robust discussion around Dijkstra's points, with several commenters offering diverse perspectives.
Several commenters agree with Dijkstra's core argument, emphasizing the inherent ambiguity and imprecision of natural language, which they see as unsuitable for the rigor and clarity required in programming. They highlight the importance of formal languages for expressing logical instructions unambiguously. Some point out that while natural language can be useful for high-level design discussions or documentation, translating it directly into executable code presents significant challenges and can lead to unreliable or unpredictable software.
A recurring theme in the comments is the distinction between "natural language programming" as envisioned in the past (i.e., literally programming in English or other natural languages) versus more modern approaches like using natural language for generating code or interacting with coding tools. Some commenters argue that Dijkstra's criticisms, while valid in the context of his time, may not fully apply to these newer paradigms. They point to advancements in natural language processing and machine learning that enable more sophisticated analysis and interpretation of natural language, potentially mitigating some of the ambiguity issues.
Some commenters bring up the importance of domain-specific languages (DSLs) as a middle ground between natural language and formal programming languages. DSLs allow developers to express logic using terminology closer to the specific problem domain while retaining the precision and unambiguity of formal languages.
A few commenters offer counterpoints to Dijkstra's arguments. Some suggest that natural language can be a valuable tool for making programming more accessible to non-experts or for rapid prototyping. Others argue that forcing programmers to think in terms of formal languages can sometimes hinder creativity and problem-solving.
One commenter points out that Dijkstra's strong stance against natural language programming might stem from his background in mathematics and formal logic, which naturally favor precise and unambiguous systems.
Overall, the comments section presents a nuanced discussion of the topic. While many agree with the fundamental points raised by Dijkstra, they also acknowledge the evolving landscape of programming and the potential for natural language to play a helpful role in certain contexts, albeit with careful consideration of its limitations. Several comments distinguish between the naive approach of directly translating natural language to code and the more nuanced possibilities afforded by modern NLP techniques, emphasizing the importance of context when interpreting Dijkstra's arguments.