Testing, when done correctly, is an important part of software development sinceit is a measure of the quality of a software in question. Most of the highly ratedsoftware projects therefore have test suites implemented that include unit tests,integration tests, and other types of tests. However, a challenge regarding the testsuite is that it needs to run each time new code changes are proposed. From thedeveloper’s perspective, it might not always be necessary to run the whole testsuite for small code changes. Previous studies have tried to tackle this probleme.g., by only running a subset of the test suite. This research investigates runningthe whole test suite of Java projects faster, by testing the Java Development Kits(JDKs) GraalVM Enterprise Edition (EE) and Community Edition (CE) againstOracle JDK and OpenJDK for Java 8 and 11. The research used the test suiteexecution time as a metric to compare the JDKs. Another metric that wasconsidered was the test suites number of test cases, used to try and find a breakingpoint for when GraalVM becomes beneficial. The tests were performed on twotest machines, where the first used 20 out of 48 tested projects and the secondused 11 out of 43 projects tested. When looking at the average of five runs,GraalVM EE 11 performed best in 11 out of 18 projects on the first test machine,compared to its closest competitor, and in 7 out of 11 projects on the second testmachine both for JDK 8 and 11. However GraalVM EE 8 did not give anybenefits to the first test machine compared to its competitors, which might indicatethat the hardware plays a vital role in the performance of GraalVM EE 8. Numberof test cases could not be used to determine a breaking point for when GraalVM isbeneficial, but it was observed that GraalVM did not show any benefits forprojects with an execution time of fewer than 39 seconds. It is observed thatGraalVM CE, does not perform well as compared to the other JDKs, and in allcases, its performance is not countable due to less non-satisfied and inefficientbehavior.

Forecasting systems are usually based on data warehouses for data strorage, and OLAP tools for historical and predictive data visualization. Aggregated predictive data could be modified. Hence, the research issue can be described as the propagation of an aggregate-based modification in hirarchies and dimensions in a data warehouse enironment. Ther exists a great number of research works on related view maintenance problems. However, to our knowledge, the impact of interactive aggregate modifications on raw data was not investigated. This CIFRE thesis is supported by ANRT and the company Anticipeo. The application of Anticipeo is a sales forecasting system that predicts future sales in order to draw appropriate business strategy in advance. By the beginning of the thesis, the customers of Anticipeo were satisfied the precision of the prediction results, but not with the response time. The work of this thesis can be generalized into two parts. The first part consists in au audit on the existing application. We proposed a methodology relying on different technical solutions. It concerns the propagation of an aggregate-based modification in a data warehouse. the second part of our work consists in the proposition of a newx allgorithms (PAM - Propagation of Aggregated-baseed Modification) with an extended version (PAM II) to efficiently propagate in aggregate-based modification. The algorithms identify and update the exact sets of source data anf other aggregated impacted by the aggregated modification. The optimized PAM II version archieves better performance compared to PAM when the use of additional semantics (e.g. dependencies) is possible. The experiments on real data of Anticipeo proved that the PAM algorithm and its extension bring better perfiormance when a backward propagation.

Les systèmes prévisionnels reposent généralemnt sur des entrepôts de données pour le stockage et sur les outils OLAP pour la visualisation. Des données prédictives agrégées pourraient être modifiées. Par conséquent, la problématique derecherche peut être décrite comme la propagation d'une modification faite sur un agrégat à travers des hiérachies et des dimensions dans un environnement d'entrepôt de données. Il existe un grand nombre de travaux de recherche sur les problèmes de maintenance de vues. Cependant, à notre connaissance, l'impact de la mise à jour interactive d'un agrégat sur les données de base n'a pas été exploré. Cette thèse CIFRE est soutenue par l'ANRT et l'entreprise Anticipeo. L'application Anticipeo est un système prévisionnel de ventes, qui prédit des ventes. Elle était précise avec des résultats de la prédiction, mais le temps de réponse était un problème. Cette thèse comporte deux parties. La première partie est d'identifier la provenance de la latence. Nous avons proposé une méthodologie s'appuyant sur différentes approches et techniques pour améliorer les performances d'une application. Cependant, la propagation d'une modification effectuée sur une agrégat dans un entrpôt de données ne pouvait pas être résolue par ces biais techniques. La deuxième partie du travail consiste en la proposition d'un nouvel algorithme (PAM - Propagation de modification basée sur une agrégat) avec une version étendue (PAM II) pour cette situation. Les algorithmes identifient et mettent àjour les ensembles exactes de données sources et d'aurtes agrégats influencés par la modification d'agrégat. La version optimisées PAM II réalise une meilleure performance par rapport à PAM quand l'utilisation d'une sémantique supplémentaire (par exemple, les dépendances) est possible. Les expériences sur des données réelles d'Anticipeo ont montré que l'algorithme PAM et son extension apportent de meilleures performances dans la propagation des mises à jour. / Forecasting systems are usually based on data warehouses for data strorage, and OLAP tools for historical and predictive data visualization. Aggregated predictive data could be modified. Hence, the research issue can be described as the propagation of an aggregate-based modification in hirarchies and dimensions in a data warehouse enironment. Ther exists a great number of research works on related view maintenance problems. However, to our knowledge, the impact of interactive aggregate modifications on raw data was not investigated. This CIFRE thesis is supported by ANRT and the company Anticipeo. The application of Anticipeo is a sales forecasting system that predicts future sales in order to draw appropriate business strategy in advance. By the beginning of the thesis, the customers of Anticipeo were satisfied the precision of the prediction results, but not with the response time. The work of this thesis can be generalized into two parts. The first part consists in au audit on the existing application. We proposed a methodology relying on different technical solutions. It concerns the propagation of an aggregate-based modification in a data warehouse. the second part of our work consists in the proposition of a newx allgorithms (PAM - Propagation of Aggregated-baseed Modification) with an extended version (PAM II) to efficiently propagate in aggregate-based modification. The algorithms identify and update the exact sets of source data anf other aggregated impacted by the aggregated modification. The optimized PAM II version archieves better performance compared to PAM when the use of additional semantics (e.g. dependencies) is possible. The experiments on real data of Anticipeo proved that the PAM algorithm and its extension bring better perfiormance when a backward propagation.

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