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Hrshikesh Arora

Publications and source records attributed to Hrshikesh Arora.

2 recordsLinked to original sources
arXiv2019

Iteratively Composing Statically Verified Traits

Static verification relying on an automated theorem prover can be very slow and brittle: since static verification is undecidable, correct code may not pass a particular static verifier. In this work we use metaprogramming to generate code that is correct by construction. A theorem prover is used only to verify initial "traits": units of code that can be used to compose bigger programs. In our work, meta-programming is done by trait composition, which starting from correct code, is guaranteed to produce correct code. We do this by extending conventional traits with pre- and post-conditions for the methods; we also extend the traditional trait composition (+) operator to check the compatibility of contracts. In this way, there is no need to re-verify the produced code. We show how our approach can be applied to the standard "power" function example, where metaprogramming generates optimised, and correct, versions when the exponent is known in advance.

cs.PL↗
arXiv2019

Separating Use and Reuse to Improve Both

Context: Trait composition has inspired new research in the area of code reuse for object oriented (OO) languages. One of the main advantages of this kind of composition is that it makes possible to separate subtyping from subclassing; which is good for code-reuse, design and reasoning. However, handling of state within traits is difficult, verbose or inelegant. Inquiry: We identify the this-leaking problem as the fundamental limitation that prevents the separation of subtyping from subclassing in conventional OO languages. We explain that the concept of trait composition addresses this problem, by distinguishing code designed for use (as a type) from code designed for reuse (i.e. inherited). We are aware of at least 3 concrete independently designed research languages following this methodology: TraitRecordJ, Package Templates and DeepFJig. Approach: In this paper, we design $42_μ$ a new language, where we improve use and reuse and support the This type and family polymorphism by distinguishing code designed for use from code designed for reuse. In this way $42_μ$ synthesise the 3 approaches above, and improves them with abstract state operations: a new elegant way to handle state composition in trait based languages. Knowledge and Grounding: Using case studies, we show that $42_μ$'s model of traits with abstract state operations is more usable and compact than prior work. We formalise our work and prove that type errors cannot arise from composing well typed code. Importance: This work is the logical core of the programming language 42. This shows that the ideas presented in this paper can be applicable to a full general purpose language. This form of composition is very flexible and could be used in many new languages.

cs.PL↗