Analysing artefacts dependencies to evolving software systems

Les logiciels sont en constante évolution, nécessitant une maintenance et un développement continus. Ils subissent des changements tout au long de leur vie, que ce soit pendant l'ajout de nouvelles fonctionnalités ou la correction de bogues. Lorsque les logiciels évoluent, leurs architectures ont tendance à se dégrader et deviennent moins adaptables aux nouvelles spécifications des utilisateurs. En effet, les architectures de ces logiciels deviennent plus complexes et plus difficiles à maintenir à cause des nombreuses dépendances entre les artefacts. Par conséquent, les développeurs doivent comprendre les dépendances entre les artefacts des logiciels pour prendre des mesures proactives qui facilitent les futurs changements et ralentissent la dégradation des architectures des logiciels. D'une part, le maintien d'un logiciel sans la compréhension des les dépendances entre ses artefacts peut conduire à l'introduction de défauts. D'autre part, lorsque les développeurs manquent de connaissances sur l'impact de leurs activités de maintenance, ils peuvent introduire des défauts de conception, qui ont un impact négatif sur l'évolution du logiciel. Ainsi, les développeurs ont besoin de mécanismes pour comprendre comment le changement d'un artefact impacte le reste du logiciel. Dans cette thèse, nous proposons trois contributions principales : La spécification de deux nouveaux patrons de changement et leurs utilisations pour fournir aux développeurs des informations utiles concernant les dépendances de co-changement. La spécification de la relation entre les patrons d'évolutions des artefacts et les fautes. La découverte de la relation entre les dépendances des anti-patrons et la prédisposition des différentes composantes d'un logiciel aux fautes.Program maintenance accounts for the largest part of the costs of any program. During maintenance activities, developers implement changes (sometimes simultaneously) on artefacts to fix bugs and to implement new requirements. Thus, developers need knowledge to identify hidden dependencies among programs artefacts and detect correlated artefacts. As programs evolved, their designs become more complex over time and harder to change. In the absence of the necessary knowledge on artefacts dependencies, developers could introduce design defects and faults that causes development and maintenance costs to rise. Therefore, developers must understand the dependencies among program artefacts and take proactive steps to facilitate future changes and minimize fault proneness. On the one hand, maintaining a program without understanding the different dependencies between their artefacts may lead to the introduction of faults. On the other hand, when developers lack knowledge about the impact of their maintenance activities, they may introduce design defects, which have a negative impact on program evolution. Thus, developers need mechanisms to understand how a change to an artefact will impact the rest of the programs artefacts and tools to detect design defects impact. In this thesis, we propose three principal contributions. The first contribution is two novel change patterns to model new co-change and change propagation scenarios. We introduce the Asynchrony change pattern, corresponding to macro co-changes, i.e., of files that co-change within a large time interval (change periods), and the Dephase change pattern, corresponding to dephase macro co-changes, i.e., macro co-changes that always happen with the same shifts in time. We present our approach, named Macocha, and we show that such new change patterns provide interesting information to developers. The second contribution is proposing a novel approach to analyse the evolution of different classes in object-oriented programs and to link different evolution behaviour to faults. In particular, we define an evolution model for each class to study the evolution and the co-evolution dependencies among classes and to relate such dependencies with fault-proneness. The third contribution concerns design defect dependencies impact. We propose a study to mine the link between design defect dependencies, such as co-change dependencies and static relationships, and fault proneness. We found that the negative impact of design defects propagate through their dependencies. The three contributions are evaluated on open-source programs

Exploring the Impact of Serverless Computing on Peer To Peer Training Machine Learning

The increasing demand for computational power in big data and machine learning has driven the development of distributed training methodologies. Among these, peer-to-peer (P2P) networks provide advantages such as enhanced scalability and fault tolerance. However, they also encounter challenges related to resource consumption, costs, and communication overhead as the number of participating peers grows. In this paper, we introduce a novel architecture that combines serverless computing with P2P networks for distributed training and present a method for efficient parallel gradient computation under resource constraints. Our findings show a significant enhancement in gradient computation time, with up to a 97.34\% improvement compared to conventional P2P distributed training methods. As for costs, our examination confirmed that the serverless architecture could incur higher expenses, reaching up to 5.4 times more than instance-based architectures. It is essential to consider that these higher costs are associated with marked improvements in computation time, particularly under resource-constrained scenarios. Despite the cost-time trade-off, the serverless approach still holds promise due to its pay-as-you-go model. Utilizing dynamic resource allocation, it enables faster training times and optimized resource utilization, making it a promising candidate for a wide range of machine learning applications

Clones and Macro co-changes

Ideally, any change that modifies the similar parts of a cloned code snippet should be propagated to all its duplicates. In practice however, consistent propagation of changes in clones does not always happen. Current evidence indicates that clone families have a 50% chance of having consistent changes. This paper measures cloning and co-changes at file level as a proxy to assess the frequency of consistent changes. Given that changes to a clone group are not necessarily propagated in the same commit transaction (i.e., late propagations), our analysis uses macro co-changes instead of the traditional definition of co-changes. Macro changes group bursts of changes that are closer among themselves than to other changes, regardless of author or message. Then, macro co-changes are sets of files that change in the same macro changes. Each cloned file is tagged depending on whether any of the files with which it macro co-changes is cloned with it (during the macro change) or not. Contrary to previous results, we discovered that most of the cloned files macro co-change only with files with which they share clones. Thus providing evidence that macro changes are appropriate to study the conjecture of clones requiring co-changes, and indicating that consistent changes might be the norm in cloned code

Analysing Anti-patterns Static Relationships with Design Patterns

Anti-patterns are motifs that are usually thought to be good solutions tosome design or implementation problems, but back-fires badly when applied. Previousstudies have reported that anti-patterns make object oriented systems hard tomaintain. Anti-patterns motifs usually have dependencies with other classes in thesystem. In this paper, we propose to analyse these dependencies (with in particulardesign patterns) in order to understand how developers can maintain programscontaining anti-patterns. To the best of our knowledge, no substantial investigationof anti-pattern dependencies with design patterns has been done before. This paperpresents the results of a study that we performed on three different systems, ArgoUML,JFreeChart, and XercesJ, written in Java, and of size ranges from 1,191to 3,325 classes, to analyse the static relationships between anti-patterns and designpatterns. We found that these relationships (1) exist, but (2) are temporaryand (3) classes participating in such relationships are more change-prone but lessfault-prone than other anti-pattern classes

Using Probabilistic Temporal Logic PCTL and Model Checking for Context Prediction

Context prediction is a promoting research topic with a lot of challenges and opportunities. Indeed, with the constant evolution of context-aware systems, context prediction remains a complex task due to the lack of formal approach. In this paper, we propose a new approach to enhance context prediction using a probabilistic temporal logic and model checking. The probabilistic temporal logic PCTL is used to provide an efficient expressivity and a reasoning based on temporal logic in order to fit with the dynamic and non-deterministic nature of the system's environment. Whereas, the probabilistic model checking is used for automatically verifying that a probabilistic system satisfies a property with a given likelihood. Our new approach allows a formal expressivity of a multidimensional context prediction. Tested on real data our model was able to achieve 78% of the future activities prediction accuracy

SPIRT: A Fault-Tolerant and Reliable Peer-to-Peer Serverless ML Training Architecture

The advent of serverless computing has ushered in notable advancements in distributed machine learning, particularly within parameter server-based architectures. Yet, the integration of serverless features within peer-to-peer (P2P) distributed networks remains largely uncharted. In this paper, we introduce SPIRT, a fault-tolerant, reliable, and secure serverless P2P ML training architecture. designed to bridge this existing gap. Capitalizing on the inherent robustness and reliability innate to P2P systems, SPIRT employs RedisAI for in-database operations, leading to an 82\% reduction in the time required for model updates and gradient averaging across a variety of models and batch sizes. This architecture showcases resilience against peer failures and adeptly manages the integration of new peers, thereby highlighting its fault-tolerant characteristics and scalability. Furthermore, SPIRT ensures secure communication between peers, enhancing the reliability of distributed machine learning tasks. Even in the face of Byzantine attacks, the system's robust aggregation algorithms maintain high levels of accuracy. These findings illuminate the promising potential of serverless architectures in P2P distributed machine learning, offering a significant stride towards the development of more efficient, scalable, and resilient applications

Clones and Macro-Co-Changes

Ideally, any change that modifies the similar parts of a cloned code snippet should be propagated to all its duplicates. In practice however, consistent propagation of changes in clones does not always happen. Current evidence indicates that clone families have a 50% chance of having consistent changes. This paper measures cloning and co-changes at file level as a proxy to assess the frequency of consistent changes. Given that changes to a clone group are not necessarily propagated in the same commit transaction (i.e., late propagations), our analysis uses macro co-changes instead of the traditional definition of co-changes. Macro changes group bursts of changes that are closer among themselves than to other changes, regardless of author or message. Then, macro co-changes are sets of files that change in the same macro changes. Each cloned file is tagged depending on whether any of the files with which it macro co-changes is cloned with it (during the macro change) or not. Contrary to previous results, we discovered that most of the cloned files macro co-change only with files with which they share clones. Thus providing evidence that macro changes are appropriate to study the conjecture of clones requiring co-changes, and indicating that consistent changes might be the norm in cloned code

Analyzing software evolution and quality by extracting Asynchrony change patterns

Change patterns describe two or more files were often changed together during the development or the maintenance of software systems. Several studies have been presented to detect change patterns and to analyze their types and their impact on software quality. In this context, we introduced the Asynchrony change pattern to describes a set of files that always change together in the same change periods, regardless developers who maintained them. In this paper, we investigate the impact of Asynchrony change pattern on design and code smells such as anti-patterns and code clones. Concretely, we conduct an empirical study by detecting Asynchrony change patterns, anti-patterns and code clones occurrences on 22 versions of four software systems and analyzing their fault-proneness. Results show that cloned files that follow the same Asynchrony change patterns have significantly increased fault-proneness with respect to other clones, and that anti-patterns following the same Asynchrony change pattern can be up to five times more risky in terms of fault-proneness as compared to other anti-patterns. Asynchrony change patterns thus seem to be strong indicators of fault-proneness for clones and anti-patterns