Olivier Barais

Sidahmed Yalles, Mohamed Handaoui, Jean-Emile Dartois et al.

One of the main objectives of Cloud Providers (CP) is to guarantee the Service-Level Agreement (SLA) of customers while reducing operating costs. To achieve this goal, CPs have built large-scale datacenters. This leads, however, to underutilized resources and an increase in costs. A way to improve the utilization of resources is to reclaim the unused parts and resell them at a lower price. Providing SLA guarantees to customers on reclaimed resources is a challenge due to their high volatility. Some state-of-the-art solutions consider keeping a proportion of resources free to absorb sudden variation in workloads. Others consider stable resources on top of the volatile ones to fill in for the lost resources. However, these strategies either reduce the amount of reclaimable resources or operate on less volatile ones such as Amazon Spot instance. In this paper, we proposed RISCLESS, a Reinforcement Learning strategy to exploit unused Cloud resources. Our approach consists of using a small proportion of stable on-demand resources alongside the ephemeral ones in order to guarantee customers SLA and reduce the overall costs. The approach decides when and how much stable resources to allocate in order to fulfill customers' demands. RISCLESS improved the CPs' profits by an average of 15.9% compared to state-of-the-art strategies. It also reduced the SLA violation time by an average of 36.7% while increasing the amount of used ephemeral resources by 19.5% on average

Amine Benelallam, Nicolas Harrand, César Soto Valero et al.

The Maven Central Repository provides an extraordinary source of data to understand complex architecture and evolution phenomena among Java applications. As of September 6, 2018, this repository includes 2.8M artifacts (compiled piece of code implemented in a JVM-based language), each of which is characterized with metadata such as exact version, date of upload and list of dependencies towards other artifacts. Today, one who wants to analyze the complete ecosystem of Maven artifacts and their dependencies faces two key challenges: (i) this is a huge data set; and (ii) dependency relationships among artifacts are not modeled explicitly and cannot be queried. In this paper, we present the Maven Dependency Graph. This open source data set provides two contributions: a snapshot of the whole Maven Central taken on September 6, 2018, stored in a graph database in which we explicitly model all dependencies; an open source infrastructure to query this huge dataset.

Charly Reux, Mathieu Acher, Djamel Eddine Khelladi et al.

With the rise of AI-based code generation, customizing existing code out of natural language instructions to modify visual results -such as figures or images -has become possible, promising to reduce the need for deep programming expertise. However, even experienced developers can struggle with this task, as it requires identifying relevant code regions (feature location), generating valid code variants, and ensuring the modifications reliably align with user intent. In this paper, we introduce vTikZ, the first benchmark designed to evaluate the ability of Large Language Models (LLMs) to customize code while preserving coherent visual outcomes. Our benchmark consists of carefully curated vTikZ editing scenarios, parameterized ground truths, and a reviewing tool that leverages visual feedback to assess correctness. Empirical evaluation with stateof-the-art LLMs shows that existing solutions struggle to reliably modify code in alignment with visual intent, highlighting a gap in current AI-assisted code editing approaches. We argue that vTikZ opens new research directions for integrating LLMs with visual feedback mechanisms to improve code customization tasks in various domains beyond TikZ, including image processing, art creation, Web design, and 3D modeling.

Mohamed Handaoui, Jean-Emile Dartois, Jalil Boukhobza et al.

Cloud data center capacities are over-provisioned to handle demand peaks and hardware failures which leads to low resources' utilization. One way to improve resource utilization and thus reduce the total cost of ownership is to offer unused resources (referred to as ephemeral resources) at a lower price. However, reselling resources needs to meet the expectations of its customers in terms of Quality of Service. The goal is so to maximize the amount of reclaimed resources while avoiding SLA penalties. To achieve that, cloud providers have to estimate their future utilization to provide availability guarantees. The prediction should consider a safety margin for resources to react to unpredictable workloads. The challenge is to find the safety margin that provides the best trade-off between the amount of resources to reclaim and the risk of SLA violations. Most state-of-the-art solutions consider a fixed safety margin for all types of metrics (e.g., CPU, RAM). However, a unique fixed margin does not consider various workloads variations over time which may lead to SLA violations or/and poor utilization. In order to tackle these challenges, we propose ReLeaSER, a Reinforcement Learning strategy for optimizing the ephemeral resources' utilization in the cloud. ReLeaSER dynamically tunes the safety margin at the host-level for each resource metric. The strategy learns from past prediction errors (that caused SLA violations). Our solution reduces significantly the SLA violation penalties on average by 2.7x and up to 3.4x. It also improves considerably the CPs' potential savings by 27.6% on average and up to 43.6%.

Nicolas Harrand, Amine Benelallam, César Soto-Valero et al.

Hyrum's law states a common observation in the software industry: "With a sufficient number of users of an API, it does not matter what you promise in the contract: all observable behaviors of your system will be depended on by somebody". Meanwhile, recent research results seem to contradict this observation when they state that "for most APIs, there is a small number of features that are actually used". We investigate this seeming paradox between the observations in industry and the research literature, with a large scale empirical study of client API relationships in one single ecosystem: Maven Central. We study the 94 most popular libraries in Maven Central, as well as the 829,410 client artifacts that declare a dependency to these libraries and that are available in Maven Central, summing up to 2.2M dependencies. Our analysis indicates the existence of a wide spectrum of API usages, with enough clients, most API types end up being used at least once. Our second key observation is that, for all libraries, there is a small set of API types that are used by the vast majority of its clients. The practical consequences of this study are two-fold: (i) it is possible for API maintainers to find an essential part of their API on which they can focus their efforts; (ii) API developers should limit the public API elements to the set of features for which they are ready to have users.

César Soto-Valero, Amine Benelallam, Nicolas Harrand et al.

Maven artifacts are immutable: an artifact that is uploaded on Maven Central cannot be removed nor modified. The only way for developers to upgrade their library is to release a new version. Consequently, Maven Central accumulates all the versions of all the libraries that are published there, and applications that declare a dependency towards a library can pick any version. In this work, we hypothesize that the immutability of Maven artifacts and the ability to choose any version naturally support the emergence of software diversity within Maven Central. We analyze 1,487,956 artifacts that represent all the versions of 73,653 libraries. We observe that more than 30% of libraries have multiple versions that are actively used by latest artifacts. In the case of popular libraries, more than 50% of their versions are used. We also observe that more than 17% of libraries have several versions that are significantly more used than the other versions. Our results indicate that the immutability of artifacts in Maven Central does support a sustained level of diversity among versions of libraries in the repository.

Ludovic Mouline, Thomas Hartmann, François Fouquet et al.

Smart systems are characterised by their ability to analyse measured data in live and to react to changes according to expert rules. Therefore, such systems exploit appropriate data models together with actions, triggered by domain-related conditions. The challenge at hand is that smart systems usually need to process thousands of updates to detect which rules need to be triggered, often even on restricted hardware like a Raspberry Pi. Despite various approaches have been investigated to efficiently check conditions on data models, they either assume to fit into main memory or rely on high latency persistence storage systems that severely damage the reactivity of smart systems. To tackle this challenge, we propose a novel composition process, which weaves executable rules into a data model with lazy loading abilities. We quantitatively show, on a smart building case study, that our approach can handle, at low latency, big sets of rules on top of large-scale data models on restricted hardware.

Fouquet Francois, Grégory Nain, Brice Morin et al.

The creation of Domain Specific Languages(DSL) counts as one of the main goals in the field of Model-Driven Software Engineering (MDSE). The main purpose of these DSLs is to facilitate the manipulation of domain specific concepts, by providing developers with specific tools for their domain of expertise. A natural approach to create DSLs is to reuse existing modeling standards and tools. In this area, the Eclipse Modeling Framework (EMF) has rapidly become the defacto standard in the MDSE for building Domain Specific Languages (DSL) and tools based on generative techniques. However, the use of EMF generated tools in domains like Internet of Things (IoT), Cloud Computing or Models@Runtime reaches several limitations. In this paper, we identify several properties the generated tools must comply with to be usable in other domains than desktop-based software systems. We then challenge EMF on these properties and describe our approach to overcome the limitations. Our approach, implemented in the Kevoree Modeling Framework (KMF), is finally evaluated according to the identified properties and compared to EMF.

Jean-Marc Jézéquel, Benoit Combemale, Olivier Barais et al.

With the growing use of domain-specific languages (DSL) in industry, DSL design and implementation goes far beyond an activity for a few experts only and becomes a challenging task for thousands of software engineers. DSL implementation indeed requires engineers to care for various concerns, from abstract syntax, static semantics, behavioral semantics, to extra-functional issues such as run-time performance. This paper presents an approach that uses one meta-language per language implementation concern. We show that the usage and combination of those meta-languages is simple and intuitive enough to deserve the term "mashup". We evaluate the approach by completely implementing the non trivial fUML modeling language, a semantically sound and executable subset of the Unified Modeling Language (UML).