A Large-Scale Industrial Case Study on Architecture-Based Software Reliability Analysis

Anomaly detection plays a critical role in large-scale industrial systems, where real-time streaming data is generated at high volumes. This research journal presents an in-depth study on developing an efficient anomaly detection algorithm specifically designed for such industrial systems. The goal is to identify abnormal patterns and deviations from normal behavior, enabling proactive maintenance, improved operational efficiency, and reduced downtime. The proposed algorithm leverages machine learning and stream processing techniques to handle the challenges associated with real- time streaming data analysis. The research covers algorithm design, implementation, evaluation, and performance analysis using large-scale datasets from industrial domains. Keywords: anomaly detection, real-time streaming data, large-scale industrial systems, machine learning, stream processing

Feature models are frequently used to capture the knowledge about configurable software systems and product lines. However, feature modeling of large-scale systems is challenging as many models are needed for diverse purposes. For instance, feature models can be used to reflect the perspectives of product management, technical solution architecture, or product configuration. Furthermore, models are required at different levels of granularity. Although numerous approaches and tools are available, it remains hard to define the purpose, scope, and granularity of feature models. In this paper we thus present experiences of developing feature models for two large-scale industrial automation software systems. Specifically, we extended an existing feature modeling tool to support models for different purposes and at multiple levels. We report results on the characteristics and modularity of the feature models, including metrics about model dependencies. We further discuss lessons learned during the modeling process.

In this article, a correlation-aided neural network (CANN) framework is proposed for distributed process monitoring of large-scale industrial automation systems. First, to model the nonlinear data relationship of multiple subsystems, a feature space is learned for each subsystem by means of neural network-based nonlinear projection. Second, different from conventional distributed monitoring methods, a distributed learning method and a novel objective function are proposed, where the influence of uncertainties is decreased by considering the correlation of variables among multiple subsystems. Third, a process monitoring method is proposed based on the CANN framework. The impact of the fault is analyzed, and the optimal monitoring performance is achieved by considering the correlation of all subsystems. To further reduce communication overhead among multiple subsystems, a tradeoff between monitoring performance and communication overhead is made based on gradients of the CANN model. Theoretical analysis demonstrates the superiority of the proposed method. The case studies on a multizone heating, ventilation, and air-conditioning system and the Tennessee Eastman benchmark process are given to evaluate and compare the effectiveness of the proposed method.

Object-Relational Mapping (ORM) provides developers a conceptual abstraction for mapping the application code to the underlying databases. ORM is widely used in industry due to its convenience; permitting developers to focus on developing the business logic without worrying too much about the database access details. However, developers often write ORM code without considering the impact of such code on database performance, leading to cause transactions with timeouts or hangs in large-scale systems. Unfortunately, there is little support to help developers automatically detect suboptimal database accesses. In this paper, we propose an automated framework to detect ORM performance anti-patterns. Our framework automatically flags performance anti-patterns in the source code. Furthermore, as there could be hundreds or even thousands of instances of anti-patterns, our framework provides sup- port to prioritize performance bug fixes based on a statistically rigorous performance assessment. We have successfully evaluated our framework on two open source and one large-scale industrial systems. Our case studies show that our framework can detect new and known real-world performance bugs and that fixing the detected performance anti- patterns can improve the system response time by up to 98%.

It is our great pleasure to welcome you to the 8th International ACM Sigsoft Conference on the Quality of Software Architectures -- QoSA'12. For almost a decade, QoSA aims at advancing the state of the art in the quality assessment and management for software architectures. Specific issues of interest are defining quality measures, evaluating and managing architecture quality, linking architecture to requirements and implementation, and preserving architecture quality throughout the lifetime of the system. Past themes for QoSA include Quality throughout the Software Lifecycle (2011), Research into Practice -- Reality and Gaps (2010), Architectures for Adaptive Software Systems (2009), and Models and Architecture (2008). The umbrella theme of QoSA 2012 is Evolving Architectures. The QoSA'12 call for papers attracted 52 abstracts leading to the final number of 44 submissions from Europe, North and South America, Australia, Asia, and Africa. The program committee accepted 11 long papers and 7 short papers that cover a variety of topics related to the quality of software architectures. In addition, the program includes two keynote speeches, first by Alberto Montresor (University of Trento, Italy) on Designing Extreme Distributed Systems: Challenges and Opportunities, and second by Johan Bendz (Convenor of the ISO/IEC-JTC1-SC7 Working Group on Architecture) on International Standards Supporting Quality in Software Architecture. QoSA is part of the federated event CompArch, this year together with "CBSE 2012: 15th International ACM SIGSOFT Symposium on Component Based Software Engineering," "ISARCS 2012: 3rd International ACM SIGSOFT Symposium on Architecting Critical Systems," "WCOP 2012: 17th International Doctoral Symposium on Components and Architecture," and "ROSS 2012: Workshop on Reusing Open-Source Components." We are grateful to the organizers of all these events for making CompArch a successful federated event on Component-based Software Engineering and Software Architecture.

In large-scale industrial recommendation systems, model checkpoints are instrumental in maintaining training goodput and numerical correctness during system failures and job preemptions. The increasing prevalence of multi-terabyte models has rendered frequent regular model checkpoints impractical, resulting in substantial lost progress when recovering from failures. As model sizes continue to grow, researchers and practitioners are compelled to investigate more efficient and scalable solutions. This paper presents DECK, a novel approach to delta model checkpointing designed for real-world industrial systems. Specifically, DECK focuses on extracting delta states with near-zero overhead, staging and streaming delta checkpoints without interrupting the training process, and merging delta checkpoints in an optimal and decoupled manner. Experimental results demonstrate that DECK achieves a 12-fold increase in checkpoint frequency while maintaining negligible impact on training throughput, thereby attaining state-of-the-art (SOTA) production performance.

After investigating the conception and features of PLC (Programmable Logic Controller) and embedded system, in this paper the conception of a Quasi-PLC is proposed, which combine the advantages of PLC and embedded system. The architecture of Quasi-PLC and a Top-Down design method with collaboration design of software and hardware for Quasi-PLC embedded control system are researched then. Finally, setting the large-scale industrial valve control system as an example, the conception of Quasi-PLC and related technologies are used in industrial valve control system, and a satisfied application effect is obtained, which proves the rationality of the conception of Quasi-PLC and related technologies.

Over the last years, home service robots have a wide range of potential applications, such as home security, patient caring, cleaning, etc. When developing robot software, one of the main challenges is to build the software architectural model. Software architecture is used throughout the software life-cycle for supporting analysis, guiding development, and acting as a roadmap for designers and implementers. Though many software architectures for robotic systems have been defined, none of them have reached all its objectives due to the great variability among systems behaviors, and still lack systematic techniques to derive the robot software architecture from its requirements model. In this paper, we present a generic architectural model for home service robots allowing for software architecture design, and preserving a continuous architectural view all along the development cycle. While avoiding the predominant decomposition problems, our approach allows for integration of the architectural components in a systematic and comprehensive way for efficient maintainability and reusability.

There is a plethora of models, techniques and tools that support model-and component-based software development of vehicular distributed embedded systems. However, a large majority of them have a limited or no support to model and specify end-to-end resource reservations on the software architectures of these systems. Resource reservations allow flexibility during the development and execution of such complex systems without jeopardizing their predictable behavior. As a result, several applications in the system that share the same hardware platform can be developed independently. In this paper we identify several requirements that any existing component model should fulfill in order to support the modeling of end-to-end resource reservations on the software architectures of such systems. As a proof of concept, we extend the Rubus Component Model (RCM) by fulfilling these requirements. RCM is used for the development of control functionality in vehicular embedded systems by several international companies. We also provide a technique to extract execution models from the software architectures of these systems with resource reservations. In order to show the usability of our technique, we model a vehicular distributed embedded system with the extended component model and extract the execution model from the software architecture augmented with end-to-end resource reservations.

Risk management poses a major challenge in the design and operation of large-scale industrial systems. The common strategy for managing risk is the rule-based approach in which automatic safety devices and standard operating procedures are developed as a counter to the potentially catastrophic effects of system malfunctions that can be anticipated. This is, however, a flawed approach. Large-scale industrial systems can be so complex that it is impossible to anticipate all eventualities. In addition, rules are irremedially incomplete as determinants of behavior. They encapsulate explicit knowledge but they cannot be used to completely specify behavior because their use always involves implicit or unarticulated knowledge. A case study of a highly reliable system with catastrophic potential is described. The development of experiential knowledge is seen as a key element in the achievement of safe and reliable procedures. The case study suggests that operators should be given direct access to critical information about the process to be controlled and should be provided with ample opportunity to develop sensitivity to this critical information. >

When developing large-scale industrial software systems engineers need to instantiate, configure, and deploy diverse reusable components. The number of component instances required depends on customer requirements only known during configuration and is typically unknown when modeling the systems’ variability. Also, the hierarchy of dynamically created component instances leads to complex dependencies between configuration decisions. Dealing with component multiplicity and hierarchy thus requires an approach capable of expressing the dependencies among dynamically instantiated components and configuration decisions. Furthermore, users need tool support for navigating the complex decision space during configuration. In this experience paper we report on applying a decision-oriented modeling approach for defining component variability, multiplicity, and hierarchy. We further present a configuration tool that guides end users through the complex decision space. We report applications of the approach to industrial software systems and describe patterns and lessons learned.

Quality of system and software architectures plays a major role in today's development to reduce the complexity of systems during design and ensure maintainability and extensibility. Metrics can provide system architects with information about the quality of the system architectures and support them to ensure the quality characteristics defined in ISO 25010. In this position paper, we look on selected metrics from code and object-oriented design analysis to use them for the evaluation of system architectures modeled with EAST-ADL. Therefore, the metrics (collections) Lines of Code, Cyclomatic Complexity, Chidamber and Kemerer and MOOD are examined for their application on EAST-ADL models.

To assure high software quality for large-scale industrial software systems, traditional approaches of software quality assurance, such as software testing and performance engineering, have been widely used within Alibaba, the world's largest retailer, and one of the largest Internet companies in the world. However, there still exists a high demand for software quality assessment to achieve high sustainability of business growth and engineering culture in Alibaba. To address this issue, we develop an industrial solution for software quality assessment by following the GQM paradigm in an industrial setting. Moreover, we integrate multiple assessment methods into our solution, ranging from metric selection to rating aggregation. Our solution has been implemented, deployed, and adopted at Alibaba: (1) used by Alibaba's Business Platform Unit to continually monitor the quality for 60+ core software systems; (2) used by Alibaba's R&D Efficiency Unit to support group-wide quality-aware code search and automatic code inspection. This paper presents our proposed industrial solution, including its techniques and industrial adoption, along with the lessons learned during the development and deployment of our solution.

Adopting an accurate anomaly detection mechanism is crucial for industrial software systems in order to prevent system outages that can deteriorate system availability. However, employing a supervised machine learning technique to detect anomalies in large production scale industrial software systems is highly impractical due to the requirement of annotated data. This raises the need for comprehensive semi-supervised and unsupervised anomaly detection mechanisms. This paper presents the application of Generative Adversarial Network (GAN) based models to detect system anomalies using semi-supervised one-class learning. We show that the use of a variant of GAN known as bidirectional GAN (BiGAN) gives augmented results when compared to the traditional GAN based anomaly detection, for the selected industrial system. Moreover, the experiments clearly show that the performance of the BiGAN has a direct correlation with the dimensions of the dataset used for training. The BiGAN even tends to outperform the well-established semi-supervised One-class SVM classifier and a prominent generative network for semi-supervised anomaly detection, Variational Autoencoders (VAEs) when the size of the feature space increases.

The large number of industrial control systems based on PLCs (Programmable Logic Controllers) available at CERN implies a huge number of programs and lines of code. The software quality assurance becomes a key point to ensure the reliability of the control systems. Static code analysis is a relatively easy-to-use, simple way to find potential faults or error-prone parts in the source code. While static code analysis is widely used for general purpose programming languages (e.g. Java, C), this is not the case for PLC program languages. We have analyzed the possibilities and the gains to be expected from applying static analysis to the PLC code used at CERN, based on the UNICOS framework. This paper reports on our experience with the method and the available tools and sketches an outline for future work to make this analysis method practically applicable.