Type checking annotation-based product lines

Abstract

Software-product-line engineering is an efficient means to generate a family of program variants for a domain from a single code base. However, because of the potentially high number of possible program variants, it is difficult to test them all and ensure properties like type safety for the entire product line. We present a product-line-aware type system that can type check an entire software product line without generating each variant in isolation. Specifically, we extend the Featherweight Java calculus with feature annotations for product-line development and prove formally that all program variants generated from a well-typed product line are well-typed. Furthermore, we present a solution to the problem of typing mutually exclusive features. We discuss how results from our formalization helped implementing our own product-line tool CIDE for full Java and report of experience with detecting type errors in four existing software-product-line implementations.

Where Do Configuration Constraints Stem From? An Extraction Approach and an Empirical Study

“…A simple approach to do this is to analyze every single possible configuration to find which ones contain errors. To avoid such an intractable brute-force approach and to avoid incompleteness from sampling strategies, we build on our recent research infrastructure, TypeChef, to analyze the entire configuration space of C code with build-time variability at once [27]- [29].…”

“…This variability-aware analysis is conceptually sound and complete with regard to a brute-force approach of preprocessing, parsing, and type checking all configurations separately. However, it is much faster since it does the analysis in a single step and exploits similarities among the implementations of different configurations; see [27]- [29] for further details.…”

Variability Mining: Consistent Semi-automatic Detection of Product-Line Features

“…We have already implemented such variability-aware type systems for Java and C in prior work (and, for a subset, formally proved that it ensures well-typedness for all variants of the product line) [31], [36]. For variability mining, we reimplemented these checks as recommendation mechanism.…”

“…For such cases, we adjusted the type system's lookup functions and check implications between the variability model and annotations using a SAT solver. To understand the recommendation mechanism, the simple model described here is sufficient; for details, we refer the interested reader to our formal discussions in [31].…”

Migrating Automotive Product Lines: A Case Study

“…Lifting has been applied to model checking [8], type checking [19], testing [20], etc. Our work fits in this category, focusing on lifting transformations.…”