Probabilistic Analysis of Loss in Interface Adapter Chaining

TL;DR

This paper introduces a probabilistic method to analyze and optimize interface adapter chains, accounting for adaptation quality and complexity, with a greedy algorithm for near-optimal solutions.

Contribution

It presents a novel probabilistic framework for analyzing interface adapter loss and demonstrates the NP-completeness of optimal chaining, proposing a practical greedy algorithm.

Findings

Probabilistic model effectively measures adaptation loss.

Optimal chaining problem is NP-complete.

Greedy algorithm provides near-optimal chains efficiently.

Abstract

Interface adapters allow applications written for one interface to be reused with another interface without having to rewrite application code, and chaining interface adapters can significantly reduce the development effort required to create the adapters. However, interface adapters will often be unable to convert interfaces perfectly, so there must be a way to analyze the loss from interface adapter chains in order to improve the quality of interface adaptation. This paper describes a probabilistic approach to analyzing loss in interface adapter chains, which not only models whether a method can be adapted but also how well methods can be adapted. We also show that probabilistic optimal adapter chaining is an NP-complete problem, so we describe a greedy algorithm which can construct an optimal interface adapter chain with exponential time in the worst case.