Change-Impact driven Agile Architecting.

Software architecture is a key factor to scale up Agile Software Development ASD in large softwareintensive systems. Currently, software architectures are more often approached through mechanisms that enable to incrementally design and evolve software architectures aka. agile architecting. Agile architecting should be a light-weight decision-making process, which could be achieved by providing knowledge to assist agile architects in reasoning about changes. This paper presents the novel solution of using change-impact knowledge as the main driver for agile architecting. The solution consists of a Change Impact Analysis technique and a set of models to assist agile architects in the change -decision-making- process by retrieving the change-impact architectural knowledge resulting from adding or changing features iteration after iteration. To validate our approach, we have put our solution into practice by running a project of a metering management system in electric power networks in an i-smart software factory

CSM-424- Evolutionary Complexity: Investigations into Software Flexibility

Flexibility has been hailed as a desirable quality since the earliest days of software engineering. Classic and modern literature suggest that particular programming paradigms, architectural styles and design patterns are more “flexible” than others but stop short of suggesting objective criteria for measuring such claims. We suggest that flexibility can be measured by applying notions of measurement from computational complexity to the software evolution process. We define evolution complexity (EC) metrics, and demonstrate that— (a) EC can be used to establish informal claims on software flexibility; (b) EC can be constant ����or linear� in the size of the change; (c) EC can be used to choose the most flexible software design policy. We describe a small-scale experiment designed to test these claims

The evolution of the laws of software evolution. A discussion based on a systematic literature review

After more than 40 years of life, software evolution should be considered as a mature field. However, despite such a long history, many research questions still remain open, and controversial studies about the validity of the laws of software evolution are common. During the first part of these 40 years the laws themselves evolved to adapt to changes in both the research and the software industry environments. This process of adaption to new paradigms, standards, and practices stopped about 15 years ago, when the laws were revised for the last time. However, most controversial studies have been raised during this latter period. Based on a systematic and comprehensive literature review, in this paper we describe how and when the laws, and the software evolution field, evolved. We also address the current state of affairs about the validity of the laws, how they are perceived by the research community, and the developments and challenges that are likely to occur in the coming years

Towards a Taxonomy of Software Evolution

Previous taxonomies of software evolution have focused on the purpose of the change rather than the underlying mechanisms. This paper proposes a taxonomy of software evolution based on the characterizing mechanisms of change and the factors that influence these mechanisms. The taxonomy is organized into the following logical groupings: temporal properties, objects of change, system properties, and change support. The ultimate goal of this taxonomy is to provide a framework that positions concrete tools, formalisms and methods within the domain of software evolution. Such a framework would considerably ease comparison between these tools, formalisms and methods. It would also allow practitioners to evaluate their potential use in particular change scenarios. As an initial step towards this taxonomy, the paper presents a framework that can be used to characterize software change support tools and to identify the factors that impact on the use of these tools. The framework is evaluated by applying it to three different change support tools and by comparing these tools based on this analysis

Toward an improved understanding of software change

Structural changes, including moving, renaming, merging and splitting are important design change decisions made by programmers. However, during the process of software evolution, this information often gets lost. Recovering instances of structural changes in the past, as well as understanding them, are essential for us to achieve a better understanding of how and why software changes. In this thesis, we propose an approach that helps to recover and understand the lost information of structural changes

Visualisations novatrices pour la compréhension de réseaux et de logiciels complexes

La visualisation d’information a le potentiel de pouvoir exploiter nos capacités visuelles, acquises au fil de centaines de millions d’années d’évolution, afin de faciliter la découverte de secrets enfouis dans les données, de nouveaux patrons ou de relations insoupçonnées. Il existe toutefois une grande variété de données, plus ou moins structurées, que l’on cherche à comprendre sous diverses perspectives. En particulier, les données sous forme de réseaux servent à modéliser des phénomènes importants, tels que les communautés sociales ou les transactions financières, mais peuvent être difficiles à représenter si les réseaux sont grands, hiérarchiques, et/ou dynamiques. Cette thèse se concentre sur la conception de nouvelles techniques de visualisation de réseaux, dans le but de faciliter la compréhension de données. Les techniques de visualisation présentes dans la littérature sont utiles dans certains contextes et comportent chacune des limitations. Néanmoins, il existe encore des possibilités inexplorées pour créer des nouvelles façons de représenter des données. La validation de ces nouvelles techniques demeure un défi. En outre, les interfaces doivent être simples à utiliser, mais aussi faciliter l’analyse et l’exploration de données. Dans le but d’étudier de nouvelles options de visualisations pour faciliter des tâches de compréhension des données, nous avons d’abord classifié les travaux antérieurs avec des taxonomies. De cette manière, nous avons aussi pu mettre en lumière des nouvelles pistes d’hybrides (c’est-à-dire, des combinaisons d’approches) potentiellement intéressantes pour visualiser des réseaux statiques et dynamiques. Les contributions présentées dans cette thèse couvrent différents aspects de la visualisation de réseaux complexes et dynamiques. D’abord, le premier chapitre se concentre sur la visualisation de réseaux statiques comportant des hiérarchies, par la combinaison d’approches. Le prototype décrit dans le deuxième chapitre permet également de combiner des représentations visuelles, mais peut être aussi utilisé afin de modéliser des graphes dynamiques. Enfin, le troisième chapitre présente une nouvelle méthode visuelle appliquée afin de tracer l’évolution de structures de conception complexes dans des logiciels (modélisés par des réseaux). Ainsi, dans le premier prototype (TreeMatrix), des parties de graphes sont montrées avec des matrices et des diagrammes noeuds-liens, alors que les arborescences sont représentées par des diagrammes en glaçons et des regroupements. Contrairement aux autres visualisations dans la littérature, cette nouvelle technique aide à montrer des réseaux denses, sans nuire à la compréhension des liens à plus haut niveau. Une expérience avec des utilisateurs a montré certains avantages afin de découvrir et organiser les liens de modules au sein d’un logiciel, en comparaison avec le logiciel commercial Lattix. Nous avons également combiné des approches de manière novatrice pour notre second prototype (DiffAni) afin de visualiser des réseaux qui évoluent dans le temps. DiffAni est le premier hybride interactif de graphes dynamiques et sa validation avec des participants a permis de faire ressortir certains avantages. Ainsi, l’utilisation d’animation doit être modérée et est surtout utile lors de mouvements significatifs. Ces résultats, avec nos taxonomies, pourraient contribuer à guider la création de nouveaux hybrides dans le futur. Le troisième prototype (IHVis) a facilité l’exploration et le traçage de structures de conception dans des logiciels en évolution (modélisés par des réseaux) à partir de répertoires de code source. Cette nouvelle visualisation a notamment révélé des cas d’introduction de points de stabilité et des refactorings, et certains participants ont aussi trouvé d’autres informations intéressantes, telles que l’extension de fonctionnalités par l’implémentation d’interfaces. En résumé, cette thèse présente des façons novatrices et utiles de visualiser des réseaux complexes et dynamiques. Nos principales contributions sont (1) l’exploration d’espaces de conception de nouvelles visualisations de réseaux à l’aide de taxonomies, (2) la conception de prototypes combinant des approches pour visualiser des réseaux hiérarchiques et dynamiques, (3) la conception d’une nouvelle méthode visuelle d’exploration des variations et des instabilités au sein de logiciels en évolution, (4) l’évaluation de ces techniques à l’aide d’expériences avec des participants

Predicting software Size and Development Effort: Models Based on Stepwise Refinement

This study designed a Software Size Model and an Effort Prediction Model, then performed an empirical analysis of these two models. Each model design began with identifying its objectives, which led to describing the concept to be measured and the meta-model. The numerical assignment rules were then developed, providing a basis for size measurement and effort prediction across software engineering projects. The Software Size Model was designed to test the hypothesis that a software size measure represents the amount of knowledge acquired and stored in software artifacts, and the amount of time it took to acquire and store this knowledge. The Effort Prediction Model is based on the estimation by analogy approach and was designed to test the hypothesis that this model will produce reasonably close predictions when it uses historical data that conforms to the Software Size Model. The empirical study implemented each model, collected and recorded software size data from software engineering project deliverables, simulated effort prediction using the jack knife approach, and computed the absolute relative error and magnitude of relative error (MRE) statistics. This study resulted in 35.3% of the predictions having an MRE value at or below twenty-five percent. This result satisfies the criteria established for the study of having at least 31 % of the predictions with a MRE of25% or less. This study is significant for three reasons. First, no subjective factors were used to estimate effort. The elimination of subjective factors removes a source of error in the predictions and makes the study easier to replicate. Second, both models were described using metrology and measurement theory principles. This allows others to consistently implement the models and to modify these models while maintaining the integrity of the models\u27 objectives. Third, the study\u27s hypotheses were validated even though the software artifacts used to collect the software size data varied significantly in both content and quality. Recommendations for further study include applying the Software Size Model to other data-driven estimation models, collecting and using software size data from industry projects, looking at alternatives for how text-based software knowledge is identified and counted, and studying the impact of project cycles and project roles on predicting effort

An approach to modelling and describing software evolution processes

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