Survey of Template-Based Code Generation

L'automatisation de la génération des artefacts textuels à partir des modèles est une étape critique dans l'Ingénierie Dirigée par les Modèles (IDM). C'est une transformation de modèles utile pour générer le code source, sérialiser les modèles dans de stockages persistents, générer les rapports ou encore la documentation. Parmi les différents paradigmes de transformation de modèle-au-texte, la génération de code basée sur les templates (TBCG) est la plus utilisée en IDM. La TBCG est une technique de génération qui produit du code à partir des spécifications de haut niveau appelées templates. Compte tenu de la diversité des outils et des approches, il est nécessaire de classifier et de comparer les techniques de TBCG existantes afin d'apporter un soutien approprié aux développeurs. L'objectif de ce mémoire est de mieux comprendre les caractéristiques des techniques de TBCG, identifier les tendances dans la recherche, et éxaminer l'importance du rôle de l'IDM par rapport à cette approche. J'évalue également l'expressivité, la performance et la mise à l'échelle des outils associés selon une série de modèles. Je propose une étude systématique de cartographie de la littérature qui décrit une intéressante vue d'ensemble de la TBCG et une étude comparitive des outils de la TBCG pour mieux guider les dévloppeurs dans leur choix. Cette étude montre que les outils basés sur les modèles offrent plus d'expressivité tandis que les outils basés sur le code sont les plus performants. Enfin, Xtend2 offre le meilleur compromis entre l'expressivité et la performance.A critical step in model-driven engineering (MDE) is the automatic synthesis of a textual artifact from models. This is a very useful model transformation to generate application code, to serialize the model in persistent storage, generate documentation or reports. Among the various model-to-text transformation paradigms, Template-Based Code Generation (TBCG) is the most popular in MDE. TBCG is a synthesis technique that produces code from high-level specifications, called templates. It is a popular technique in MDE given that they both emphasize abstraction and automation. Given the diversity of tools and approaches, it is necessary to classify and compare existing TBCG techniques to provide appropriate support to developers. The goal of this thesis is to better understand the characteristics of TBCG techniques, identify research trends, and assess the importance of the role of MDE in this code synthesis approach. We also evaluate the expressiveness, performance and scalability of the associated tools based on a range of models that implement critical patterns. To this end, we conduct a systematic mapping study of the literature that paints an interesting overview of TBCG and a comparative study on TBCG tools to better guide developers in their choices. This study shows that model-based tools offer more expressiveness whereas code-based tools performed much faster. Xtend2 offers the best compromise between the expressiveness and the performance

Analysis and Transformation of Configurable Systems

Static analysis tools and transformation engines for source code belong to the standard equipment of a software developer. Their use simplifies a developer's everyday work of maintaining and evolving software systems significantly and, hence, accounts for much of a developer's programming efficiency and programming productivity. This is also beneficial from a financial point of view, as programming errors are early detected and avoided in the the development process, thus the use of static analysis tools reduces the overall software-development costs considerably. In practice, software systems are often developed as configurable systems to account for different requirements of application scenarios and use cases. To implement configurable systems, developers often use compile-time implementation techniques, such as preprocessors, by using #ifdef directives. Configuration options control the inclusion and exclusion of #ifdef-annotated source code and their selection/deselection serve as an input for generating tailor-made system variants on demand. Existing configurable systems, such as the linux kernel, often provide thousands of configuration options, forming a huge configuration space with billions of system variants. Unfortunately, existing tool support cannot handle the myriads of system variants that can typically be derived from a configurable system. Analysis and transformation tools are not prepared for variability in source code, and, hence, they may process it incorrectly with the result of an incomplete and often broken tool support. We challenge the way configurable systems are analyzed and transformed by introducing variability-aware static analysis tools and a variability-aware transformation engine for configurable systems' development. The main idea of such tool support is to exploit commonalities between system variants, reducing the effort of analyzing and transforming a configurable system. In particular, we develop novel analysis approaches for analyzing the myriads of system variants and compare them to state-of-the-art analysis approaches (namely sampling). The comparison shows that variability-aware analysis is complete (with respect to covering the whole configuration space), efficient (it outperforms some of the sampling heuristics), and scales even to large software systems. We demonstrate that variability-aware analysis is even practical when using it with non-trivial case studies, such as the linux kernel. On top of variability-aware analysis, we develop a transformation engine for C, which respects variability induced by the preprocessor. The engine provides three common refactorings (rename identifier, extract function, and inline function) and overcomes shortcomings (completeness, use of heuristics, and scalability issues) of existing engines, while still being semantics-preserving with respect to all variants and being fast, providing an instantaneous user experience. To validate semantics preservation, we extend a standard testing approach for refactoring engines with variability and show in real-world case studies the effectiveness and scalability of our engine. In the end, our analysis and transformation techniques show that configurable systems can efficiently be analyzed and transformed (even for large-scale systems), providing the same guarantees for configurable systems as for standard systems in terms of detecting and avoiding programming errors

Automating Black-Box Property Based Testing

<p>Black-box property based testing tools like QuickCheck allow developers to write elegant logical specifications of their programs, while still permitting unrestricted use of the same language features and libraries that simplify writing the programs themselves. This is an improvement over unit testing because a single property can replace a large collection of test cases, and over more heavy-weight white-box testing frameworks that impose restrictions on how properties and tested code are written. In most cases the developer only needs to write a function returning a boolean, something any developer is capable of without additional training. </p> <p> This thesis aims to further lower the threshold for using property based testing by automating some problematic areas, most notably generating test data for user defined data types. Writing procedures for random test data generation by hand is time consuming and error prone, and most fully automatic algorithms give very poor random distributions for practical cases. </p><p> Several fully automatic algorithms for generating test data are presented in this thesis, along with implementations as Haskell libraries. These algorithms all fit nicely within a framework called sized functors, allowing re-usable generator definitions to be constructed automatically or by hand using a few simple combinators. </p><p> Test quality is another difficulty with property based testing. When a property fails to find a counterexample there is always some uncertainty in the strength of the property as a specification. To address this problem we introduce a black-box variant of mutation testing. Usually mutation testing involves automatically introducing errors (mutations) in the source code of a tested program to see if a test suite can detect it. Using higher order functions, we mutate functions without accessing their source code. The result is a very light-weight mutation testing procedure that automatically estimates property strength for QuickCheck properties. </p

A Roadmap for HEP Software and Computing R&D for the 2020s

Particle physics has an ambitious and broad experimental programme for the coming decades. This programme requires large investments in detector hardware, either to build new facilities and experiments, or to upgrade existing ones. Similarly, it requires commensurate investment in the R&D of software to acquire, manage, process, and analyse the shear amounts of data to be recorded. In planning for the HL-LHC in particular, it is critical that all of the collaborating stakeholders agree on the software goals and priorities, and that the efforts complement each other. In this spirit, this white paper describes the R&D activities required to prepare for this software upgrade.Peer reviewe

Towards A Practical High-Assurance Systems Programming Language

Writing correct and performant low-level systems code is a notoriously demanding job, even for experienced developers. To make the matter worse, formally reasoning about their correctness properties introduces yet another level of complexity to the task. It requires considerable expertise in both systems programming and formal verification. The development can be extremely costly due to the sheer complexity of the systems and the nuances in them, if not assisted with appropriate tools that provide abstraction and automation. Cogent is designed to alleviate the burden on developers when writing and verifying systems code. It is a high-level functional language with a certifying compiler, which automatically proves the correctness of the compiled code and also provides a purely functional abstraction of the low-level program to the developer. Equational reasoning techniques can then be used to prove functional correctness properties of the program on top of this abstract semantics, which is notably less laborious than directly verifying the C code. To make Cogent a more approachable and effective tool for developing real-world systems, we further strengthen the framework by extending the core language and its ecosystem. Specifically, we enrich the language to allow users to control the memory representation of algebraic data types, while retaining the automatic proof with a data layout refinement calculus. We repurpose existing tools in a novel way and develop an intuitive foreign function interface, which provides users a seamless experience when using Cogent in conjunction with native C. We augment the Cogent ecosystem with a property-based testing framework, which helps developers better understand the impact formal verification has on their programs and enables a progressive approach to producing high-assurance systems. Finally we explore refinement type systems, which we plan to incorporate into Cogent for more expressiveness and better integration of systems programmers with the verification process

Programming Languages and Systems

This open access book constitutes the proceedings of the 30th European Symposium on Programming, ESOP 2021, which was held during March 27 until April 1, 2021, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2021. The conference was planned to take place in Luxembourg and changed to an online format due to the COVID-19 pandemic. The 24 papers included in this volume were carefully reviewed and selected from 79 submissions. They deal with fundamental issues in the specification, design, analysis, and implementation of programming languages and systems