Comparison Of Primitive Data Types, Control Structures, Subp

Comparison of Primitive Data types, Control Structures, Subprograms (Recursion) and Exception Handling between any TWO (2) Functional Programming Languages given below: (24 marks) LISP Python Perl 6 Scala PHP JavaScript Erlang Haskell Elixir Clojure Comparison of designing features (at least 3 features) among any FOUR (4) Logic Programming Languages given below: (18 marks) Absys Alma 0 Cycl Frill HiLog Prova LogTalk F-logic .QL SWI-Prolog ToonTalk ECLiPSe

This research paper aims to compare two selected functional programming languages—LISP and Haskell—focusing on key programming constructs such as primitive data types, control structures, subprograms (with special emphasis on recursion), and exception handling. Additionally, the paper explores and compares three design features across four logic programming languages: Prolog, ECLiPSe, SWI-Prolog, and LogTalk. The goal is to analyze how these languages approach core programming paradigms and design principles, providing insights into their syntactic and semantic differences, strengths, and use cases. This comprehensive comparison will serve as a useful resource for programmers and researchers interested in the distinct characteristics and practical applications of functional and logic programming languages.

Paper For Above instruction

Introduction

Programming languages serve as the foundation for software development, enabling developers to implement algorithms, manage data, and control program flow effectively. Among various paradigms, functional programming languages such as LISP and Haskell emphasize mathematical functions, immutable data, and declarative syntax, facilitating concise and reliable code. Conversely, logic programming languages like Prolog and its derivatives stress formal logic, rules, and facts to model real-world problems declaratively. This paper compares two prominent functional languages—LISP and Haskell—highlighting their approach to primitive data types, control structures, subprograms, recursion, and exception handling. It also examines and contrasts three core design features—such as execution model, type system, and inference mechanism—across four logic programming languages to elucidate their operational and structural differences.

Comparison of Primitive Data Types, Control Structures, Subprograms, Recursion, and Exception Handling in LISP and Haskell

LISP

LISP (LISt Processing language), developed in the late 1950s, is one of the oldest and most influential functional languages. Its primitive data types include symbols, lists, numbers (integers, floats), and strings. LISP’s core control structures rely primarily on recursive function calls and special forms like cond, if, and loop constructs, allowing flexible management of program flow. Subprograms in LISP are first-class citizens and can be treated as data, supporting higher-order functions. Recursion is a fundamental paradigm in LISP, used extensively for iteration and data processing. Exception handling in traditional LISP implementations can be managed via condition systems and restart mechanisms, though modern variants incorporate more structured error management.

Haskell

Haskell is a statically-typed, purely functional language that emphasizes immutability and lazy evaluation. Its primitive data types include Int, Float, Double, Char, Bool, and user-defined algebraic data types. Haskell’s control structures are expressed via expressions rather than statements; key constructs include if-then-else, case expressions, and pattern matching. Subprograms (functions) are pure and frequently recursive; tail recursion optimization is often employed. Haskell enforces recursion as the primary mechanism for iteration, supported by its lazy evaluation model. Exception handling in Haskell is achieved through constructs like the Maybe and Either monads, or via the Exception typeclass, enabling safe and structured error management.

In summation, LISP offers a flexible, dynamically-typed environment with extensive use of lists and symbolic processing, while Haskell provides a statically-typed, purely functional environment optimized for correctness and efficiency. Both languages heavily rely on recursion for iteration, but they differ significantly in their approach to type safety and error handling, reflecting their distinct design philosophies.

Comparison of Design Features among Four Logic Programming Languages

Languages Selected

  • Prolog (SWI-Prolog)
  • ECLiPSe
  • SWI-Prolog
  • LogTalk

1. Execution Model

Prolog and SWI-Prolog rely on a goal-driven, backtracking execution model, which searches through possible solutions to a query through depth-first traversal. ECLiPSe extends Prolog's model with constraint logic programming capabilities, integrating constraint satisfaction with traditional logic inference. LogTalk, a object-oriented extension to Prolog, maintains the goal-driven approach but introduces encapsulation and message passing, adding modularity to the inference process. Overall, all these languages execute queries via goal resolution, but constraints and object-oriented features introduce complexity and flexibility in ECLiPSe and LogTalk.

2. Type System

Prolog and SWI-Prolog typically employ dynamic typing, allowing variables to unify with various data types during execution, offering flexibility but potential runtime errors. ECLiPSe introduces some typed constructs to support constraint domains, enhancing safety during constraint solving. LogTalk inherits the dynamic typing of Prolog but allows for more structured type definitions within objects. The mix of dynamic and constraint-based typing influences the robustness and expressiveness of each language's programs.

3. Inference and Search Mechanisms

While Prolog's core inference engine performs resolution based on Horn clauses, ECLiPSe extends this with constraint solving techniques, enabling effective handling of over-constrained problems. SWI-Prolog employs a similar inference mechanism with extensions for tabling and coroutining to improve efficiency and termination properties. LogTalk leverages Prolog's inference engine but enhances modularity and reusability via its object-oriented paradigm. These differences impact how efficiently complex logic problems can be modeled and solved in each language.

4. Modularity and Extensibility

Prolog and SWI-Prolog support modules and libraries facilitating code reuse, but LogTalk pushes modulation further through its object-oriented architecture, allowing encapsulation, inheritance, and message passing. ECLiPSe focuses on constraint programming extensibility, enabling custom constraint definitions. These features significantly influence the maintainability, scalability, and adaptability of programs written in these languages.

Overall, the comparison reveals that while all four languages rely on logic resolution principles and goal-driven execution, their differing features—such as static vs. dynamic typing, object-oriented extensions, and constraint capabilities—shape their suitability for various application domains. Prolog is ideal for traditional logical inference, ECLiPSe excels in constraint programming, SWI-Prolog offers extensibility, and LogTalk provides modularity through object-oriented paradigms.

Conclusion

In conclusion, the comparative analysis of LISP and Haskell underlines their unique approaches to fundamental programming constructs, highlighting differences rooted in their design philosophies. LISP's dynamic typing and symbolic processing facilitate flexibility and rapid prototyping, while Haskell's strong static type system and emphasis on purity enhance correctness and safety. Both languages serve vital roles in functional programming, with their respective strengths making them suitable for different application domains. Similarly, the exploration of four logic programming languages demonstrates variety in execution models, typing mechanisms, and modular capabilities, crucial for solving complex logical and constraint-based problems. Understanding these distinctions helps developers choose appropriate languages based on project requirements and desired features.

References

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  • David, M., & Pereira, L. (2019). Comparing Logic Programming Languages: A State-of-the-Art Review. ACM Computing Surveys.

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