The Object Management Group's (OMG) Model Driven Architecture (MDA) strategy envisages a world where models play a more direct role in software production, being amenable to manipulation and transformation by machine. Model Driven Engineering (MDE) is wider in scope than MDA. MDE combines process and analysis with architecture. This article sets out a framework for model driven engineering, which can be used as a point of reference for activity in this area. It proposes an organisation of the modelling 'space' and how to locate models in that space. It discusses different kinds of mappings between models. It explains why process and architecture are tightly connected. It discusses the importance and nature of tools. It identifies the need for defining families of languages and transformations, and for developing techniques for generating/configuring tools from such definitions. It concludes with a call to align metamodelling with formal language engineering techniques.

Model Driven Engineering

The main reason behind failure of lots of software is poor quality thus estimation software quality become an important task in software industry. An early estimation towards pre-released software quality plays an important role in increasing probability of project success. By analyzing the metric data we can forecast the quality of the object oriented system. Software metrics are necessary to identify where the resources are needed; they are a crucial source of information for decision making. This paper represents proposed model for estimation quality of software product.

A hierarchical model for object-oriented design quality assessment

https://zenodo.org/record/1997449/files/SS18.pdf

A survey on software smells

The software engineering community has proposed numerous approaches for making software self-adaptive. These approaches take inspiration from machine learning and control theory, constructing software that monitors and modifies its own behavior to meet goals. Control theory, in particular, has received considerable attention as it represents a general methodology for creating adaptive systems. Control-theoretical software implementations, however, tend to be ad hoc. While such solutions often work in practice, it is difficult to understand and reason about the desired properties and behavior of the resulting adaptive software and its controller. This paper discusses a control design process for software systems which enables automatic analysis and synthesis of a controller that is guaranteed to have the desired properties and behavior. The paper documents the process and illustrates its use in an example that walks through all necessary steps for self-adaptive controller synthesis. (Less)

/pdf/software-engineering-meets-control-theory-3n8i8lapks.pdf

Software Engineering Meets Control Theory

Every year man-made and natural disasters impact the lives of millions of people. The frequency of occurrence of such disasters is steadily increasing since the last 50 years, and this has resulted in considerable loss of life, destruction of infrastructure, and social and economic disruption. A focussed and comprehensive solution is needed encompassing all aspects, including early detection of disaster scenarios, prevention, recovery, and management to minimize the losses. This survey paper presents a critical analysis of the existing methods and technologies that are relevant to a disaster scenario, such as WSN, remote sensing technique, artificial intelligence, IoT, UAV, and satellite imagery, to encounter the issues associated with disaster monitoring, detection, and management. In case of emergency conditions arising out of a typical disaster scenario, there is a strong likelihood that the communication networks will be partially disrupted; thus the alternate networks can play a vital role in disaster detection and management. It focuses on the role of the alternate networks and the associated technologies in maintaining connectivity in various disaster scenarios. It presents a comprehensive study on multiple disasters such as landslide, forest fire, and an earthquake based on the latest technologies to monitor, detect, and manage the various disasters. It focuses on several parameters that are necessary for disaster detection and monitoring and offers appropriate solutions. It also touches upon big data analytics for disaster management. Several techniques are explored, along with their merits and demerits. Open challenges are highlighted, and possible future directions are given.

Multi-hazard disaster studies: monitoring, detection, recovery, and management, based on emerging technologies and optimal techniques

Identifying and removing the causes of poor performance in software systems are complex problems due to a variety of factors to take into account. Nowadays these problems are usually tackled after the software deployment only with human-based means, which frequently boil down to developer skills and previous experiences. Performance antipatterns can be used to cope with these problems since they capture typical design patterns that are known leading to performance problems, as well as refactoring actions that can be taken to remove them.The goal of this paper is to introduce an approach that allows the refactoring of architectural models, based on antipatterns, that aims at providing performance improvement. To this end, we use a Role-Based Modeling Language to represent: (i) antipattern problems as Source Role Models (SRMs), and (ii) antipattern solutions as Target Role Models (TRMs). Hence, SRM-TRM pairs represent new instruments in the hands of developers to achieve architectural model refactorings aimed at removing sources of performance problems. Model refactoring for antipattern removal can be in fact obtained by replacing an SRM with the corresponding TRM. This approach has been applied to a case study in the e-commerce domain, whose experimental results demonstrate its effectiveness.

Antipattern-based model refactoring for software performance improvement

Context Software refactoring is a common practice aimed at addressing requirements or fixing bugs during the software development. While refactoring related to functional requirements has been widely studied in the last few years, non-functional-driven refactoring is still critical, mostly because non-functional characteristics of software are hard to assess and appropriate refactoring actions can be difficult to identify. In the context of performance, which is the focus of this paper, antipatterns represent effective instruments to tackle this issue, because they document common mistakes leading to performance problems as well as their solutions. Objective In order to effectively reuse the knowledge beyond performance antipatterns, automation is required to detect and remove them. In this paper we introduce a framework that enables, in an unique tool context, the refactoring of software models driven by performance antipattern detection and removal. Method We have implemented, within the EPSILON platform, detection rules and refactoring actions on UML models for a set of well-known performance antipatterns. By exploiting the EPSILON languages to check properties and apply refactoring on models, we enable three types of refactoring sessions. Results We experiment our framework on a Botanical Garden Management System to show, on one side, that antipatterns can effectively drive software refactoring towards models that satisfy performance requirements and, on the other side, that the automation introduced by EPSILON-based sessions enables to inspect multiple paths and to propose a variety of solutions. Conclusion This work demonstrates that automation in performance-driven software model refactoring can be beneficial, and that performance antipatterns can be powerful instruments in the hands of software engineers for detecting (and solving) performance problems usually hidden to traditional bottleneck analysis. This work also opens the road to the integration of well-known techniques for software refactoring driven by functional requirements with novel techniques addressing non-functional requirements like performance.

Performance-driven software model refactoring

Self-adaptive techniques have been introduced in the last few years to tackle the growing complexity of software systems, where a major complexity factor leans on their dynamic nature subject to sudden and unpredictable changes that can heavily impact on the software architecture quality. Non-functional models, as generated from architectural descriptions of software, have been proven as effective instruments to support designers meeting non-functional requirements since the early architectural phases. However, such models still lack of intrinsic support for adaptable software. Goal of this paper is to extend the modeling capabilities to the case of software adaptation aimed at satisfying performance requirements. In particular, we illustrate how control theory can solve the problem of keeping within pre-defined ranges the indices of a Queueing Network model (such as queue length) through software adaptation actions (such as replacing software services with less resource-demanding ones), while the model is subject to disturbances (such as workload and/or operational profile variations). For this goal we first introduce a library of Modelica components that represent Queueing Network (QN) elements with adaptable parameters (that can be used as knobs for adaptation actions). Then we use such components to build ``adaptable`` QN models that are subject to disturbances. Finally, in the same framework, we introduce controllers that drive the QN adaptation. We demonstrate the soundness of our approach on a simple representative example in two ways: (i) on one end, we provide a formal proof of controller performance guarantees, and (ii) on the other end we show the sensitivity over time of software adaptation actions to different (types and intensities of) disturbances.

https://dl.acm.org/doi/pdf/10.1145/2737182.2737187

Control Theory for Model-based Performance-driven Software Adaptation

Several approaches have been introduced in the last few years to tackle the problem of interpreting model-based performance analysis results and translating them into architectural feedback. Typically the interpretation can take place by browsing either the software model or the performance model. In this paper, we compare two approaches that we have recently introduced for this goal: one based on the detection and solution of performance antipatterns, and another one based on bidirectional model transformations between software and performance models. We apply both approaches to the same example in order to illustrate the differences in the obtained performance results. Thereafter, we raise the level of abstraction and we discuss the pros and cons of working on the software side and on the performance side.

Software model refactoring based on performance analysis: better working on software or performance side?

Performance antipatterns are well-known bad design practices that lead to software products suffering from poor performance. A number of performance antipatterns has been defined and classified and refactoring actions have also been suggested to remove them. In the last few years, we have dedicated some effort to the detection and refactoring of performance antipatterns in software models. A specific characteristic of performance antipatterns is that they contain numerical parameters that may represent thresholds referring to either performance indices (e.g., a device utilization) or design features (e.g., number of interface operations of a software component). In this paper, we analyze the influence of such thresholds on the capability of detecting and refactoring performance antipatterns. In particular, (i) we analyze how a set of detected antipatterns may change while varying the threshold values and (ii) we discuss the influence of thresholds on the complexity of refactoring actions. With the help of a leading example, we quantify the influence using precision and recall metrics.

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Davide Arcelli

Papers

Antipattern-based model refactoring for software performance improvement

Performance-driven software model refactoring

Control Theory for Model-based Performance-driven Software Adaptation

Software model refactoring based on performance analysis: better working on software or performance side?

Experimenting the Influence of Numerical Thresholds on Model-based Detection and Refactoring of Performance Antipatterns