Manually creating test cases is time consuming and error prone. Search-based software testing (SBST) can help automate this process and thus to reduce time and effort and increase quality by automatically generating relevant test cases. Previous research have mainly focused on static programming languages with simple test data inputs such as numbers. In this work we present an approach for search-based software testing for dynamic programming languages that can generate test scenarios and both simple and more complex test data. This approach is implemented as a tool in and for the dynamic programming language Ruby. It uses an evolutionary algorithm to search for tests that gives structural code coverage. We have evaluated the system in an experiment on a number of code examples that differ in complexity and the type of input data they require. We compare our system with the results obtained by a random test case generator. The experiment shows, that the presented approach can compete with random testing and, for many situations, quicker finds tests and data that gives a higher structural code coverage.

Updates are a natural part of the life cycle of an application. The traditional way of updating an application by stopping it, replacing it with the new version and restarting it is lacking in many ways. There have been previous research in the field of dynamic software updates (DSU) that attempt to salvage this problem by updating the app while running. Most of the previous research have focused on static languages like C and Java, research with dynamic languages have been lacking. This thesis takes advantage of the dynamic features of JavaScript in order to allow for dynamic updates of applications for mobile devices. The solution is implemented and used to answer questions about how correctness can be ensured and what state transfer needs to be manually written by a programmer. The conclusion is that most failures that occur as the result of an update and is in need of a manually written state transfer can be put into one of three categories. To verify correctness of an update tests for these types of failures should be performed.

Dans le but d’optimiser la représentation en mémoire des enregistrements Scheme dans le compilateur Gambit, nous avons introduit dans celui-ci un système d’annotations de type et des vecteurs contenant une représentation abrégée des enregistrements. Ces derniers omettent la référence vers le descripteur de type et l’entête habituellement présents sur chaque enregistrement et utilisent plutôt un arbre de typage couvrant toute la mémoire pour retrouver le vecteur contenant une référence. L’implémentation de ces nouvelles fonctionnalités se fait par le biais de changements au runtime de Gambit. Nous introduisons de nouvelles primitives au langage et modifions l’architecture existante pour gérer correctement les nouveaux types de données. On doit modifier le garbage collector pour prendre en compte des enregistrements contenants des valeurs hétérogènes à alignements irréguliers, et l’existence de références contenues dans d’autres objets. La gestion de l’arbre de typage doit aussi être faite automatiquement. Nous conduisons ensuite une série de tests de performance visant à déterminer si des gains sont possibles avec ces nouvelles primitives. On constate une amélioration majeure de performance au niveau de l’allocation et du comportement du gc pour les enregistrements typés de grande taille et des vecteurs d’enregistrements typés ou non. De légers surcoûts sont toutefois encourus lors des accès aux champs et, dans le cas des vecteurs d’enregistrements, au descripteur de type. / In order to optimize the in memory representation of Scheme records in the Gambit compiler, we introduce a type annotation system on record fields. We also introduce flat vector of records containing an abbreviated representation of those records. These vectors omit the header and reference to the type descriptor on contained records and use a type tree spanning the whole memory to recover the type as needed from an internal pointer. The implementation of the new functionnalities is done through changes in the Gambit runtime. We add new primitives to the language and modify the existing architecture to correctly handle the new data types in a way transparent that is transparent to the user. To do so, we modify the garbage collector to account to account for the existance of internal references and of heterogenous records whose fields may not be alligned to a word and need not be boxed. We also have to automatically and systematically update hte type tree to reflect the live vectors. To asses our implementation’s performance, we run a serie of benchmarks. We measure significant gains on allocation time and space with both typed records and contained records. We also measure a minor overhead in access costs on typed fields and major loss on accesses to the type descriptor of contained records.

The master's thesis deals with design of a dynamic reflective prototype-based language. First, basic principles of this language group are explained and well known representatives are described. Then languages for scientific computing are shortly discussed. Next section of the thesis describes in detail the proposed programming language, its grammar and semantics. Principles of type checking and inheritance are explained. Thesis also demonstrates implementation of basic control structures known from other languages. Next section shows design of virtual machine for the language described before. Section explains used computational model, organization of the object memory and internal representation of important structures of the designed language. Finally, dynamic type checking, compiler and compilation of typical structures to the virtual machine internal code are discussed.

Le langage de programmation Python est aujourd'hui parmi les plus populaires au monde grâce à son accessibilité ainsi que l'existence d'un grand nombre de librairies standards. Paradoxalement, Python est également reconnu pour ses performances médiocres lors de l'exécution de nombreuses tâches. Ainsi, l'écriture d’implémentations efficaces du langage est nécessaire. Elle est toutefois freinée par la sémantique complexe de Python, ainsi que par l’absence de sémantique formelle officielle. Pour régler ce problème, nous présentons une sémantique formelle pour Python axée sur l’implémentation de compilateurs optimisants. Cette sémantique est écrite de manière à pouvoir être intégrée et analysée aisément par des compilateurs déjà existants. Nous introduisons également semPy, un évaluateur partiel de notre sémantique formelle. Celui-ci permet d'identifier et de retirer automatiquement certaines opérations redondantes dans la sémantique de Python. Ce faisant, semPy génère une sémantique naturellement plus performante lorsqu'exécutée. Nous terminons en présentant Zipi, un compilateur optimisant pour le langage Python développé avec l'assistance de semPy. Sur certaines tâches, Zipi offre des performances compétitionnant avec celle de PyPy, un compilateur Python reconnu pour ses bonnes performances. Ces résultats ouvrent la porte à des optimisations basées sur une évaluation partielle générant une implémentation spécialisée pour les cas d'usage fréquent du langage. / Python is among the most popular programming language in the world due to its accessibility and extensive standard library. Paradoxically, Python is also known for its poor performance on many tasks. Hence, more efficient implementations of the language are required. The development of such optimized implementations is nevertheless hampered by the complex semantics of Python and the lack of an official formal semantics. We address this issue by presenting a formal semantics for Python focussed on the development of optimizing compilers. This semantics is written as to be easily reusable by existing compilers. We also introduce semPy, a partial evaluator of our formal semantics. This tool allows to automatically target and remove redundant operations from the semantics of Python. As such, semPy generates a semantics which naturally executes more efficiently. Finally, we present Zipi, a Python optimizing compiler developped with the aid of semPy. On some tasks, Zipi displays performance competing with those of PyPy, a Python compiler known for its good performance. These results open the door to optimizations based on a partial evaluation technique which generates specialized implementations for frequent use cases.