Abstract

Control systems operate industrial plants to accomplish stakeholder objectives like achieving production targets, complying with environmental ordinances, handling faults, etc. Such stakeholder objectives get realised by identifying and executing valid control actions on the plant’s control system. E.g., to achieve fault management a command is fired to place machines in a fault mode when the plant is under an error state. Arriving at such control actions is a non-trivial task demanding a detailed understanding of the plant’s structure and behaviour. Besides, it is also essential to verify the consequences of such control actions relative to other cross-cutting objectives and plant behaviour. E.g., to fulfil fault management objectives, the action to set machines in fault mode may affect production goals due to the machine unavailability. Hence, validation of control actions is vital before executing them using the actual plant’s control system. With digital twin technologies (DT), it is now possible to verify the implications of such control actions against a plant’s behaviour and objectives in a simulated environment without affecting the actual plant operations. DTs get developed autonomously as one-off solutions to simulate and validate plant control actions in the current state of practice, demanding high efforts and domain expertise. Our paper proposes a knowledge-driven approach enabling automation in DT development. The result of our approach is an auto-generated digital twin that pro-actively mimics the plant’s control system behaviour and helps with the validation of control actions before their execution. We use this approach to build three fault management DTs in a power plant. The application of our approach significantly reduces the manual efforts and development time to build such DTs.

Abstract

This Full Paper in the Innovative Practice category begins by asking “can algorithms be thought of and taught as dynamical systems?” Our exploration of this idea — Algodynamics — is guided by a vision to achieve convergence between computing and engineering education. The engineering sciences share a common conceptual vocabulary originating in dynamical systems: state spaces, flows, actions, invariants, fixed points, convergence, etc. The goal of algodynamics is to build, ab initio, a framework for understanding and teaching algorithms using concepts from dynamics. This allows us to teach computing and algorithms as an engineering science. Engineers work with models. In algodynamics, models are expressed using transition systems rather than as pseudo- code or programs. This allows a crisp representation of two important classes of computation: sequential algo- rithms as ‘discrete flows’ (iterative systems) and interactive applications as ‘action flows’ (transition systems). The focus of this paper is on the first of these classes, algorithms, but using ideas from the second, viz., tran- sition systems. In this framework, algorithms emerge as convergent iterative systems. Due to their non-interactivity, iterative systems may be hard to understand. The student may trace through the algorithm, but to know how the parts of the algorithm work together requires ‘opening up’ the algorithm and situating it within the more general class of interactive systems. Doing so helps the student to understand the machinery of an algorithm in an incremen- tal and modular way. The student interactively solves the algorithmic problem, experimenting with various strategies along the way. At each stage of the design, interactivity is traded for automation

Abstract

The annual ACM Innovations in Software Engineering Conference (ISEC) held its 14th edition online during 25–27th February, 2021. Since 2019, the conference has included a PhD symposium. The aim of the symposium is to offer PhD scholars in early stages of their research work a platform to present their work and also to get feedback from the audience. This report from the symposium co-chairs briefly summarises the effort involved in organising the symposium and a brief summary of the papers presented at the symposium.

Abstract

Online education has witnessed a spike during the COVID-19 pandemic[1], due to which it is important to be performance efficient and serve the content in a reasonable time. In this case study of Virtual Labs, an e-learning platform we analyse and propose a set of approaches which helps in improving the website’s performance. Virtual Labs is an e-learning website for virtual experiments in topics on science and engineering. These experiments are in the form of static webpages. This paper delineates the steps we followed to improve the performance of the static e-learning website. It shares insights into some of the concepts which can help improve the website performance.

Abstract

Model checking is now a standard technology for verifying large and complex systems. While there are a range of tools and techniques to verify various properties of a system under consideration, in this work, we restrict our attention to safety checking procedures using explicit state space generation. The necessary hardware resources required in this approach depends on the model complexity and the resulting state transition graph that gets generated. This cannot be estimated apriori. For reasonably realistic models, the available main memory in even high end servers may not be sufficient. Hence, we have to use distributed safety verification approaches on a cluster of nodes. However, the problem of estimating the minimum number of nodes in the cluster for the verification procedure to complete successfully remains unsolved. In this paper, we propose a dynamically scalable model checker using an actor based architecture. Using the proposed approach, an end user can invoke a model checker hosted on a cloud platform in a push button fashion. Our safety verification procedures automatically expands the cluster by requesting more virtual machines from the cloud provider. Finally, the user gets to pay only for the hardware resources he rented for the duration of the verification procedure. We refer to this as Model Checking as Service. We approach this problem by proposing an asynchronous algorithm for safety checking in actor framework. The actor based approach allows for scaling the resources on a need basis and redistributes the work load transparently through state migration. We tested our approach by developing a distributed version of SpinJA model checker using Akka actor framework. We conducted our experiments on Google Cloud Engine (GCE) platform wherein we scale our resources automatically using the GCE API. On large models such as anderson.8 from BEEM benchmark suite, our approach reduced the model checking cost in dollars by 8.6x while reducing the wall clock time to complete the safety checking procedure 5.5x times.

Abstract

The importance of algorithms and data structures in computer science curricula has been amply recognized.For many students, however, gaining a good understanding of algorithms remains a challenge.Because of the automated nature of sequential algorithms there is an inherent tension in directly applying the ‘learning by doing’ approach. This partly explains the limitations of efforts like algorithm animationand code tracing.Algodynamics, the approach we propose and advocate, situates algorithms within the framework of transi-tion systems and their dynamics and offers an attractive approach for teaching algorithms. Algodynamics starts with the premise that the key ideas underlying an algorithm can be identified and packaged into interactive transition systems. The algorithm when ‘opened up’, reveals a transition system, shorn of most control aspects, enriched instead with interaction. The design of an algorithm can be carried out by constructing a series of interactive systems, progressively trading interactivity with automation. The setransition systems constitute a family of notional machines.We illustrate the algodynamics approach by considering Bubblesort. A sequence of five interactive tran-sition systems culminate in the classic Bubblesort algorithm. The exercise of constructing the individualsystems also pays off when coding Bubblesort: a highly modular implementation whose primitives are bor-rowed from the transition systems. The transition systems used for Bubblesort have been implemented as interactive experiments. These web based implementations are easy to build. The simplicity and flexibility afforded by the algodynamics framework makes it an attractive option to teach algorithms in an interactive way.

Abstract

With increasing computing power, capacity and internet connectivity, the ability to learn has crossed classroom boundaries in the form of online learning. The adoption rate and effectiveness of online learning continues to be an area of research. Collaborative and Cooperative (CoCo) learning involves a joint effort by students to understand and solve problems or tasks. Adoption of open source software has also increased with technology-aided collaboration and cooperation. Leveraging the practices of crowdsourced software development, we propose CoCo Learning approach for technical education using virtual labs. About 145 undergraduate engineering students from various institutes participated in our study for a period of one full semester. About 92% of the students conducted an average of 24 virtual experiments with this approach. Nearly 90% of the faculty from these institutes also confirmed that their students' understanding of courses has improved while delivering tangible software artifacts as outcomes.

Abstract

We examine the mutual exclusion problem of concurrency through the systematic application of modern feedback control theory, by revisiting the classical problem involving mutual exclusion: the Generalised Dining Philosophers problem. The result is a modular development of the solution using the notions of system and system composition in a formal setting that employs simple equational reasoning. The modular approach separates the solution architecture from the algorithmic minutiae and has the benefit of simplifying the design and correctness proofs. Two variants of the problem are considered: centralised and distributed topology with N philosophers. In each case, solving the Generalised Dining Philosophers reduces to designing an appropriate feedback controller.

Abstract

Domain Specific Modeling Languages (DSML) significantly improve productivity in designing Computer Based System (CBS), by enabling them to be modeled at higher levels of abstraction. It is common for large and complex systems with distributed teams, to use DSMLs, to express and communicate designs of such systems uniformly, using a common language. DSMLs enable domain experts, with no or minimal software development background, to model solutions, using the language and terminologies used in their respective domains. Although, there are already a number of DSMLs available for modeling CBSs, their need is felt strongly across multiple domains, which still are not well supported with DSMLs. Developing a new DSML, however, is non trivial, as it requires (a) significant knowledge about the domain for which the DSML needs to be developed, as well as (b) skills to create new languages. In the current practice, DSMLs are developed by experts, who have substantial understanding of the domain of interest and strong background in computer science. One of the many challenges in the development of DSMLs, is the collection of domain knowledge and its utilization, based on which the abstract syntax, the backbone of the DSML is defined. There is a clear gap in the current state of art and practice, with respect to overcoming this challenge. We propose a methodology, which makes it easier for people with different backgrounds such as domain experts, solution architects, to contribute towards defining the abstract syntax of the DSML. The methodology outlines a set of steps to systematically capture knowledge about the domain of interest, and use that to arrive at the abstract syntax of the DSML. The key contribution of our work is in abstracting a CBS from a domain into a Domain Specific Machine, embodied in domain specific concepts. The methodology outlines, how the Domain Specific Machine, when coupled with guidelines from current practices of developing DSMLs, results in the definition of the abstract syntax of the intended DSML. We discuss our methodology in detail, in this paper.

Abstract

The focus of the ongoing Digital and Industry 4.0 revolution is on re-engineering business operations to take advantage of various technologies, including robotics. Conceptualizing, say, a robotics solution to automate aspects of warehouse operations involves multiple activities: understanding the problem space in sufficient detail to identify the right automation opportunities; working through the space of possible solution options and developing the solution design; and building a prototype solution with sufficient functional detail to enable customer experts to assess its suitability with respect to multiple concerns, and its impact on the business processes and environment. Only after that can a project be initiated to engineer and deploy the production solution, while making the necessary changes to the business system. With current practice, conceptualization and prototyping typically takes several months and considerable manual effort. In this paper, we present an environment for rapid prototyping of robotics solutions that facilitates a knowledge-centric approach based on capability composition. The environment enables systematic capture of functional domain knowledge, modular composition of solution space capabilities, and expression of the solution concept using a constrained natural language. Detailed functional simulators are generated automatically from the resulting design. This results in high customer confidence in the solution, substantial reductions in cycle time, and productivity gains due to modular reusability of solution knowledge and components.