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Several approaches have recently used automated techniques to generate architecture design alternatives by means of optimization techniques. These approaches aim at improving an initial architecture with respect to quality aspects, such as performance, reliability, or maintainability. In this context, each optimization experiment usually produces a different set of architecture alternatives that is characterized by specific settings. As a consequence, the designer is left with the task of comparing such sets to identify the settings that lead to better solution sets for the problem. To assess the quality of solution sets, multi-objective optimization commonly relies on quality indicators. Among these, the quality indicator for the maximum spread estimates the diversity of the generated alternatives, providing a measure of how much of the solution space has been explored. However, the maximum spread indicator is computed only on the objective space and does not consider architectural information (e.g., components structure, design decisions) from the architectural space. In this paper, we propose a quality indicator for the spread that assesses the diversity of alternatives by taking into account architectural features. To compute the spread, we rely on a notion of distance between alternatives according to the way they were generated during the optimization. We demonstrate how our architectural quality indicator can be applied to a dataset from the literature., Comment: 17th European Conference on Software Architecture (ECSA 2023), 8 pages

As organizations increasingly migrate their applications to the cloud, the optimization of microservices architectures becomes imperative for achieving sustainability goals. Nonetheless, sustainable deployments may increase costs and deteriorate performance, thus the identification of optimal tradeoffs among these conflicting requirements is a key objective not easy to achieve. This paper introduces a novel approach to support cloud deployment of microservices architectures by targeting optimal combinations of application performance, deployment costs, and power consumption. By leveraging genetic algorithms, specifically NSGA-II, we automate the generation of alternative architectural deployments. The results demonstrate the potential of our approach through a comprehensive assessment of the Train Ticket case study.

Context: Software model optimization is a process that automatically generates design alternatives, typically to enhance quantifiable non-functional properties of software systems, such as performance and reliability. Multi-objective evolutionary algorithms have shown to be effective in this context for assisting the designer in identifying trade-offs between the desired non-functional properties. Objective: In this work, we investigate the effects of imposing a time budget to limit the search for design alternatives, which inevitably affects the quality of the resulting alternatives. Method: The effects of time budgets are analyzed by investigating both the quality of the generated design alternatives and their structural features when varying the budget and the genetic algorithm (NSGA-II, PESA2, SPEA2). This is achieved by employing multi-objective quality indicators and a tree-based representation of the search space. Results: The study reveals that the time budget significantly affects the quality of Pareto fronts, especially for performance and reliability. NSGA-II is the fastest algorithm, while PESA2 generates the highest-quality solutions. The imposition of a time budget results in structurally distinct models compared to those obtained without a budget, indicating that the search process is influenced by both the budget and algorithm selection. Conclusions: In software model optimization, imposing a time budget can be effective in saving optimization time, but designers should carefully consider the trade-off between time and solution quality in the Pareto front, along with the structural characteristics of the generated models. By making informed choices about the specific genetic algorithm, designers can achieve different trade-offs., Comment: arXiv admin note: substantial text overlap with arXiv:2212.08385

Software architecture optimization aims to enhance non-functional attributes like performance and reliability while meeting functional requirements. Multi-objective optimization employs metaheuristic search techniques, such as genetic algorithms, to explore feasible architectural changes and propose alternatives to designers. However, the resource-intensive process may not always align with practical constraints. This study investigates the impact of designer interactions on multi-objective software architecture optimization. Designers can intervene at intermediate points in the fully automated optimization process, making choices that guide exploration towards more desirable solutions. We compare this interactive approach with the fully automated optimization process, which serves as the baseline. The findings demonstrate that designer interactions lead to a more focused solution space, resulting in improved architectural quality. By directing the search towards regions of interest, the interaction uncovers architectures that remain unexplored in the fully automated process.

Software systems continuously evolve due to new functionalities, requirements, or maintenance activities. In the context of software evolution, software refactoring has gained a strategic relevance. The space of possible software refactoring is usually very large, as it is given by the combinations of different refactoring actions that can produce software system alternatives. Multi-objective algorithms have shown the ability to discover alternatives by pursuing different objectives simultaneously. Performance of such algorithms in the context of software model refactoring is of paramount importance. Therefore, in this paper, we conduct a performance analysis of three genetic algorithms to compare them in terms of performance and quality of solutions. Our results show that there are significant differences in performance among the algorithms (e.g., PESA2 seems to be the fastest one, while NSGA-II shows the least memory usage)., Comment: 19th European Workshop on Performance Engineering (EPEW 2023)

The continuous software engineering paradigm is gaining popularity in modern development practices, where the interleaving of design and runtime activities is induced by the continuous evolution of software systems. In this context, performance assessment is not easy, but recent studies have shown that architectural models evolving with the software can support this goal. In this paper, we present a mapping study aimed at classifying existing scientific contributions that deal with the architectural support for performance-targeted continuous software engineering. We have applied the systematic mapping methodology to an initial set of 215 potentially relevant papers and selected 66 primary studies that we have analyzed to characterize and classify the current state of research. This classification helps to focus on the main aspects that are being considered in this domain and, mostly, on the emerging findings and implications for future research

Microservices are quite widely impacting on the software industry in recent years. Rapid evolution and continuous deployment represent specific benefits of microservice-based systems, but they may have a significant impact on non-functional properties like performance. Despite the obvious relevance of this property, there is still a lack of systematic approaches that explicitly take into account performance issues in the lifecycle of microservice-based systems. In such a context of evolution and re-deployment, Model-Driven Engineering techniques can provide major support to various software engineering activities, and in particular they can allow managing the relationships between a running system and its architectural model. In this paper, we propose a model-driven integrated approach that exploits traceability relationships between the monitored data of a microservice-based running system and its architectural model to derive recommended refactoring actions that lead to performance improvement. The approach has been applied and validated on two microservice-based systems, in the domain of e-commerce and ticket reservation, respectively, whose architectural models have been designed in UML profiled with MARTE.

Over the years, Software Quality Engineering has increased interest, demonstrated by significant research papers published in this area. Determining when a software artifact is qualitatively valid is tricky, given the impossibility of providing an objective definition valid for any perspective, context, or stakeholder. Many quality model solutions have been proposed that reference specific quality attributes in this context. However, these approaches do not consider the context in which the artifacts will operate and the stakeholder's perspective who evaluate its validity. Furthermore, these solutions suffer from the limitations of being artifact-specific and not extensible. In this paper, we provide a generic and extensible mechanism that makes it possible to aggregate and prioritize quality attributes. The user, taking into account his perspective and the context in which the software artifact will operate, is guided in defining all the criteria for his quality model. The management of these criteria is then facilitated through Multi-Criteria Decision Making (MCDM). In addition, we present the PRETTEF model, a concrete instance of the proposed approach for assessing and selecting MVC frameworks., Comment: 8 pages -- 3rd International Workshop on Model-Driven Engineering for Software Architecture (MDE4SA 2023)

Software quality estimation is a challenging and time-consuming activity, and models are crucial to face the complexity of such activity on modern software applications. In this context, software refactoring is a crucial activity within development life-cycles where requirements and functionalities rapidly evolve. One main challenge is that the improvement of distinctive quality attributes may require contrasting refactoring actions on software, as for trade-off between performance and reliability (or other non-functional attributes). In such cases, multi-objective optimization can provide the designer with a wider view on these trade-offs and, consequently, can lead to identify suitable refactoring actions that take into account independent or even competing objectives. In this paper, we present an approach that exploits NSGA-II as the genetic algorithm to search optimal Pareto frontiers for software refactoring while considering many objectives. We consider performance and reliability variations of a model alternative with respect to an initial model, the amount of performance antipatterns detected on the model alternative, and the architectural distance, which quantifies the effort to obtain a model alternative from the initial one. We applied our approach on two case studies: a Train Ticket Booking Service, and CoCoME. We observed that our approach is able to improve performance (by up to 42\%) while preserving or even improving the reliability (by up to 32\%) of generated model alternatives. We also observed that there exists an order of preference of refactoring actions among model alternatives. We can state that performance antipatterns confirmed their ability to improve performance of a subject model in the context of many-objective optimization. In addition, the metric that we adopted for the architectural distance seems to be suitable for estimating the refactoring effort., Comment: Accepted for publication in Information and Software Technologies. arXiv admin note: substantial text overlap with arXiv:2107.06127

The estimation and improvement of quality attributes in software architectures is a challenging and time-consuming activity. On modern software applications, a model-based representation is crucial to face the complexity of such activity. One main challenge is that the improvement of distinctive quality attributes may require contrasting refactoring actions on the architecture, for instance when looking for trade-off between performance and reliability (or other non-functional quality attributes). In such cases, multi-objective optimization can provide the designer with a more complete view on these trade-offs and, consequently, can lead to identify suitable refactoring actions that take into account independent or even competing objectives. In this paper, we present open challenges and research directions to fill current gaps in the context of multi-objective software architecture optimization., Comment: 4 pages, 1 figure -- 3rd International Workshop on Model-Driven Engineering for Software Architecture (MDE4SA 2023)

Architecture optimization is the process of automatically generating design options, typically to enhance software's quantifiable quality attributes, such as performance and reliability. Multi-objective optimization approaches have been used in this situation to assist the designer in selecting appropriate trade-offs between a number of non-functional features. Through automated refactoring, design alternatives can be produced in this process, and assessed using non-functional models. This type of optimization tasks are hard and time- and resource-intensive, which frequently hampers their use in software engineering procedures. In this paper, we present our optimization framework where we examined the performance of various genetic algorithms. We also exercised our framework with two case studies with various levels of size, complexity, and domain served as our test subjects., Comment: 4 pages, 1 figure, 20th -- IEEE International Conference on Software Architecture - poster track

Software model optimization is the task of automatically generate design alternatives, usually to improve quality aspects of software that are quantifiable, like performance and reliability. In this context, multi-objective optimization techniques have been applied to help the designer find suitable trade-offs among several non-functional properties. In this process, design alternatives can be generated through automated model refactoring, and evaluated on non-functional models. Due to their complexity, this type of optimization tasks require considerable time and resources, often limiting their application in software engineering processes. In this paper, we investigate the effects of using a search budget, specifically a time limit, to the search for new solutions. We performed experiments to quantify the impact that a change in the search budget may have on the quality of solutions. Furthermore, we analyzed how different genetic algorithms (i.e., NSGA-II, SPEA2, and PESA2) perform when imposing different budgets. We experimented on two case studies of different size, complexity, and domain. We observed that imposing a search budget considerably deteriorates the quality of the generated solutions, but the specific algorithm we choose seems to play a crucial role. From our experiments, NSGA-II is the fastest algorithm, while PESA2 generates solutions with the highest quality. Differently, SPEA2 is the slowest algorithm, and produces the solutions with the lowest quality., Comment: 8 pages, 4 figures, and 3 tables -- 48th Euromicro Conference Series on Software Engineering and Advanced Applications - MDEML track

Microbenchmarking is a widely used form of performance testing in Java software. A microbenchmark repeatedly executes a small chunk of code while collecting measurements related to its performance. Due to Java Virtual Machine optimizations, microbenchmarks are usually subject to severe performance fluctuations in the first phase of their execution (also known as warmup). For this reason, software developers typically discard measurements of this phase and focus their analysis when benchmarks reach a steady state of performance. Developers estimate the end of the warmup phase based on their expertise, and configure their benchmarks accordingly. Unfortunately, this approach is based on two strong assumptions: (i) benchmarks always reach a steady state of performance and (ii) developers accurately estimate warmup. In this paper, we show that Java microbenchmarks do not always reach a steady state, and often developers fail to accurately estimate the end of the warmup phase. We found that a considerable portion of studied benchmarks do not hit the steady state, and warmup estimates provided by software developers are often inaccurate (with a large error). This has significant implications both in terms of results quality and time-effort. Furthermore, we found that dynamic reconfiguration significantly improves warmup estimation accuracy, but still it induces suboptimal warmup estimates and relevant side-effects. We envision this paper as a starting point for supporting the introduction of more sophisticated automated techniques that can ensure results quality in a timely fashion., Comment: Published in Empirical Software Engineering (EMSE)

Axions, hypothetical particles theorized to solve the strong CP-problem, are presently being considered as strong candidates as cold dark matter constituents. The signal power of resonant-based axion detectors, known as haloscopes, is directly proportional to their quality factor $Q$. In this paper, the impact of the use of superconductors in the performances of the haloscopes is studied by evaluating the obtainable $Q$. In particular, the surface resistance $R_s$ of NbTi, Nb$_3$Sn, YBa$_2$Cu$_3$O$_{7-\delta}$ and FeSe$_{0.5}$Te$_{0.5}$ is computed in the frequency, magnetic field and temperature ranges of interest, starting from the measured vortex motion complex resistivity and screening lengths of these materials. From $R_s$ the quality factor $Q$ of a cylindrical haloscope with copper conical bases and superconductive lateral wall, operating with the TM$_{010}$ mode, is evaluated and used to perform a comparison of the performances of the different materials. Both YBa$_2$Cu$_3$O$_{7-\delta}$ and FeSe$_{0.5}$Te$_{0.5}$ are shown to improve the measurement sensitivity by almost an order of magnitude with respect to a whole Cu cavity, while NbTi is shown to be suitable only at lower frequencies (<10 GHz). Nb$_3$Sn can give an intermediate improvement in the whole spectrum of interest., Comment: 15 pages, 5 figures, submitted to Instruments journal (published by MDPI)

Software quality estimation is a challenging and time-consuming activity, and models are crucial to face the complexity of such activity on modern software applications. One main challenge is that the improvement of distinctive quality attributes may require contrasting refactoring actions on an application, as for trade-off between performance and reliability. In such cases, multi-objective optimization can provide the designer with a wider view on these trade-offs and, consequently, can lead to identify suitable actions that take into account independent or even competing objectives. In this paper, we present an approach that exploits the NSGA-II multi-objective evolutionary algorithm to search optimal Pareto solution frontiers for software refactoring while considering as objectives: i) performance variation, ii) reliability, iii) amount of performance antipatterns, and iv) architectural distance. The algorithm combines randomly generated refactoring actions into solutions (i.e., sequences of actions) and compares them according to the objectives. We have applied our approach on a train ticket booking service case study, and we have focused the analysis on the impact of performance antipatterns on the quality of solutions. Indeed, we observe that the approach finds better solutions when antipatterns enter the multi-objective optimization. In particular, performance antipatterns objective leads to solutions improving the performance by up to 15% with respect to the case where antipatterns are not considered, without affecting the solution quality on other objectives.