Software Engineering Approach Research Articles

Verified synthesis of optimal safety controllers for human-robot collaboration

We present a tool-supported approach for the synthesis, verification and validation of the control software responsible for the safety of the human-robot interaction in manufacturing processes that use collaborative robots. In human-robot collaboration, software-based safety controllers are used to improve operational safety, e.g., by triggering shutdown mechanisms or emergency stops to avoid accidents. Complex robotic tasks and increasingly close human-robot interaction pose new challenges to controller developers and certification authorities. Key among these challenges is the need to assure the correctness of safety controllers under explicit (and preferably weak) assumptions. Our controller synthesis, verification and validation approach is informed by the process, risk analysis, and relevant safety regulations for the target application. Controllers are selected from a design space of feasible controllers according to a set of optimality criteria, are formally verified against correctness criteria, and are translated into executable code and validated in a digital twin. The resulting controller can detect the occurrence of hazards, move the process into a safe state, and, in certain circumstances, return the process to an operational state from which it can resume its original task. We show the effectiveness of our software engineering approach through a case study involving the development of a safety controller for a manufacturing work cell equipped with a collaborative robot.

Towards privacy compliance: A design science study in a small organization

Complying with privacy regulations has taken on new importance with the introduction of the EU’s General Data Protection Regulation (GDPR) and other privacy regulations. Privacy measures are becoming a paramount requirement demanding software organizations’ attention as recent privacy breaches such as the Capital One data breach affected millions of customers. Software organizations, however, struggle with achieving privacy compliance. In particular, there is a lack of research into the organizational practices and challenges involved in compliance, particularly for small and medium enterprises (SMEs), which represent a sizeable portion of organizations. Many SMEs use a continuous software engineering (CSE) approach, which introduces additional adoption and application challenges. For example, the fast pace of CSE makes it harder for SMEs that are already more resource constrained to prioritize non-functional requirements such as privacy.

Fuzzing-based hard-label black-box attacks against machine learning models

Machine learning models are vulnerable to adversarial examples. We study the most realistic hard-label black-box attacks in this paper. The main limitation of the existing attacks is that they need a large number of model queries, making them inefficient and even infeasible in practice. Inspired by the very successful fuzz testing approach in traditional software engineering and computer security domains, we propose fuzzing-based hard-label black-box attacks against machine learning models. We design an AdvFuzzer to explore multiple paths between a source image and a guidance image, and design a LocalFuzzer to explore the nearby space around a given input for identifying potential adversarial examples. We demonstrate that our fuzzing attacks are feasible and effective in generating successful adversarial examples with significantly reduced number of model queries and L0 distance. More interestingly, given a successful example generated by either our or other attacks, LocalFuzzer can immediately generate more successful adversarial examples even with smaller L2 distance from the source example.

Transformation²: Making software engineering accountable for sustainability

Software engineering, as a central practice of digitalization, needs to become accountable for sustainability. In light of the ecological crises and the tremendous impact of digital systems on reshaping economic and social arrangements - often with negative side-effects - we need a sustainability transformation of the digital transformation. However, this is a complex and long-term task. In this article we combine an analysis of accountability arrangements in software engineering and a model of sustainability transformations to trace how certain dynamics are starting to make software engineering accountable for sustainability in the technological, cultural, economic and governance domains. The article discusses existing approaches for sustainable software engineering and software engineering for sustainability, traces emerging discourses that connect digitalization and sustainability, highlights new digital business models that may support sustainability and shows governance efforts to highlight “green and digital” policy problems. Yet, we argue that these are so far niche dynamics and that a sustainability transformation requires a collective and long-lasting effort to engender systemic changes. The goal should be to create varied accountability arrangements for sustainability in software engineering which is embedded in complex ways in society and economy.

The “Socialized Architecture”: A Software Engineering Approach for a New Cloud

Today, the cloud means a revolution within the Internet revolution. However, an oligopoly sustaining the cloud may not be the best solution, since ethical problems such as privacy or even transferring data sovereignty could eventually happen. Our research, coined as the "socialized architecture," presents a novel disruptive approach to completely transform the cloud as we know it today. The approach follows ideas already working in the field of volunteer computing, since it tries to socialize spare computing power in the infraused hardware that institutions and normal people own. However, our solution is completely different to current ones, since it does not create hyper-specialized muscles in client machines. The solution is new since it proposes a software engineering approach for developing “socialized services”, which, leveraging an asynchronous interaction model, creates a network of lightweight microservices that can be dynamically allocated and replicated through the network. The use of state-of-the-art patterns, such as Command Query Responsibility Segregation, helps to isolate domain events and persistence needs, while an API Gateway addresses communication. All previous ideas were tested through a complete and functional proof of concept, which is a prototype called Circle implementing a social network. Circle has been useful to expose problems that need to be addressed. The results of the assessment confirm, in our view, that it is worth to start this new field of work.

Towards Achieving AI Quality With a Systematic Software Engineering Approach

Towards Achieving AI Quality With a Systematic Software Engineering Approach

Ontology Driven Testing Strategies for IoT Applications

Internet-of-Things (IoT) has attained a major share in embedded software development. The new era of specialized intelligent systems requires adaptation of customized software engineering approaches. Currently, software engineering has merged the development phases with the technologies provided by industrial automation. The improvements are still required in testing phase for the software developed to IoT solutions. This research aims to assist in developing the testing strategies for IoT applications, therein ontology has been adopted as a knowledge representation technique to different software engineering processes. The proposed ontological model renders 101 methodology by using Protégé. After completion, the ontology was evaluated in three-dimensional view by the domain experts of software testing, IoT and ontology engineering. Satisfied results of the research are showed in interest of the specialists regarding proposed ontology development and suggestions for improvements. The Proposed reasoning-based ontological model for development of testing strategies in IoT application contributes to increase the general understanding of tests in addition to assisting for the development of testing strategies for different IoT devices.

Software Engineering in Healthcare

Abstract: As technology is advancing and affecting every phase of human life, health care facilities are also getting improved with the introduction of new advanced, customized, and patient-centric approaches in software engineering. Engineers all around the world are working to create unique and efficient ways for the prevention and early detection of diseases along with improving the patients’ experience while using these technologies. With the gain in computing power and access to a wide range of software, software engineers are working on technologies such as Big Data, AI, Data Science, and Cybersecurity to improve the existing methodologies and pave the way for patient-centric approaches and knowledge translation.

MRDRL-ROS: a Multi Robot Deep Reinforcement Learning Platform based on Robot Operating System

Deep reinforcement learning (DRL) has greatly improved the intelligence of AI in recent years and the community has proposed several common software to facilitate the development of DRL. However, in robotics the utility of common DRL software is limited and the development is time-consuming due to the complexity of various robot software. In this paper, we propose a software engineering approach leveraging modularity to facilitate robot DRL development. The platform decouples learning environment into task, simulator and hierarchical robot modules, which in turn enables diverse environment generation using existing modules as building blocks, regardless of the underlying robot software details. Experimental results show that our platform provides composable environment building, introduces high module reuse and efficiently facilitates robot DRL.

AVA: A component-oriented abstraction layer for virtual plug&produce automation systems engineering

The prevailing system and software model in automation systems engineering is defined by the IEC 61131 norm. It is to date the best way we know how to express low-level logic and manipulate electrical hardware signals. However, the exponential technological growth is continuing to raise the expectations on what automation systems are supposed to be capable of doing. Fulfilling rising requirements and managing the exploding complexity requires a systematic support for high-level descriptions, structuring, and communication, which the original approach was not built to provide. This work proposes the introduction of an abstraction layer, a component-container infrastructure, defined on top of standard system and software models in automation and mirroring the world of cyber–physical systems, where independent components are interconnected to realize the systems’ purpose by using each other’s functionalities. The concept is implemented in the form of a domain-specific modeling language, applying a classical two-level Model-driven Software Engineering (MDSE) approach. By engineering distinct industrial use cases in accordance with the proposed approach, it is shown that the defined abstractions and mechanisms are capable of expressing the nuances of software design in different domains and can enable the streamlining of the automation systems engineering workflow into a virtual plug&produce process.

A Service-Oriented Component-Based Framework for Dynamic Reconfiguration Modeling Targeting SystemC/TLM

To deal with the complex design issues of Dynamically Reconfigurable Systems-on-Chip (DRSoCs), it is extremely relevant to raise the abstraction level in which models are expressed. A high abstraction level allows great flexibility and reusability while bypassing low-level implementation details. In this context, model-driven engineering (MDE) provides support to build and transform precise and structured models for a particular purpose at different levels of abstraction. Indeed, high-level models are successively refined to low-level models until reaching the executable ones. Thus, this paper presents an MDE-based framework for DRSoCs design enabling the transformation of UML/MARTE specifications to SystemC/TLM implementation. To achieve a high degree of expressiveness for modeling dynamic reconfiguration, we use a suitable software engineering approach based on service-oriented component architecture. Since MARTE does not cover the common features of dynamic reconfiguration domain and service orientation concepts, new stereotypes are created by refinement to add missing capabilities to the profile. Likewise, SystemC does not provide native support for dynamic reconfiguration, thus leading us to adopt a design pattern based solution for DRSoCs implementation in compliance with standards. The proposed framework is validated through a reconfigurable active 3-way crossover case study in which we demonstrate the practicability of the approach by gradual model transformations with reduced implementation effort and significant design productivity gain.

A Service-Oriented Approach for Sensing in the Internet of Things: Intelligent Transportation Systems and Privacy Use Cases

This paper presents a Sensing-as-a-Service run-time Service Oriented Architecture (SOA), called 3SOA, for the development of Internet of Things (IoT) applications. 3SOA aims to allow interoperability among various IoT platforms and support service-oriented modelling at high levels of abstraction where fundamental SOA theories and techniques are fully integrated into a practical software engineering approach. 3SOA abstracts the dependencies of the middleware programming model from the application logic. This abstraction allows the development efforts to focus on writing the application logic independently from hardware platforms, middleware, and languages in which applications are programmed. To achieve this result, IoT objects are treated as independent entities that may interact with each other using a well-defined message exchange sequence. Each object is defined by the services it provides and the coordination protocol it supports. Objects are then able to coordinate their resources to address the global objectives of the system. To practically validate our proposals, we demonstrate an intelligent transportation system and data privacy functional prototypes as proof of concepts. The use cases show that 3SOA and the presented abstraction language allow the amalgamation of macroprogramming and node-centric programming to develop real-time and efficient applications over IoT.

The entrepreneurial logic of startup software development: A study of 40 software startups

Software startups are an essential source of innovation and software-intensive products. The need to understand product development in startups and to provide relevant support are highlighted in software research. While state-of-the-art literature reveals how startups develop their software, the reasons why they adopt these activities are underexplored. This study investigates the tactics behind software engineering (SE) activities by analyzing key engineering events during startup journeys. We explore how entrepreneurial mindsets may be associated with SE knowledge areas and with each startup case. Our theoretical foundation is based on causation and effectuation models. We conducted semi-structured interviews with 40 software startups. We used two-round open coding and thematic analysis to describe and identify entrepreneurial software development patterns. Additionally, we calculated an effectuation index for each startup case. We identified 621 events merged into 32 codes of entrepreneurial logic in SE from the sample. We found a systemic occurrence of the logic in all areas of SE activities. Minimum Viable Product (MVP), Technical Debt (TD), and Customer Involvement (CI) tend to be associated with effectual logic, while testing activities at different levels are associated with causal logic. The effectuation index revealed that startups are either effectuation-driven or mixed-logics-driven. Software startups fall into two types that differentiate between how traditional SE approaches may apply to them. Effectuation seems the most relevant and essential model for explaining and developing suitable SE practices for software startups.

Smart system for monitoring ammonium nitrate storage warehouse

<span>Ammonium nitrate (AN, NH4NO3) is one of the materials used in chemical industry and in agriculture. AN is not considered a fire risk but it is a strong oxidizing agent that can lead to fire and explosion, so, the ammonium nitrate must be stored under specific environmental conditions to avoid the occurrence of dangerous situations. In this paper, a smart monitoring system for Ammonium nitrate storage is developed. The system consists of three-parts: monitoring chemicals system part (MCS), warehouse monitoring part (WMP), and emergency part (EP). MCS includes several hardware components: Arduino microcontroller, Ds18b20 sensor, MQ sensors, system for mobile communication (GSM), global position system (GPS), buzzer, and LCD. Two types of alarms are designed, the first one is for the WMP that to receive the alarm when temperature is exceeds than a specific threshold in chemical warehouse. The second one is for EP. It represents the receiver part to receive the alarm with the location of the place that indicates to certain gases leakage that could cause a fire or explosion. Also, a buzzer is listening and display the sensor state on LCD. The system follows the object-oriented software engineering (OOSE) approach using unified modeling language (UML).</span>

A model‐driven approach for semantic web service modeling using web service modeling languages

AbstractThe service‐oriented software engineering approach has been increasingly utilized to design and develop complex distributed systems. Exploiting semantic web technologies to increase utilization of the web services at the semantic level leads to create semantic web services (SWSs). This paper proposes a model‐driven architecture (MDA) to model the SWS and to transform it from a high‐level modeling language, such as the Unified Modeling Language (UML), to a low‐level semantic description, such as the Web Service Modeling Language (WSML). To annotate all aspects of the SWS, a UML profile is provided by extending the UML metamodel. Some stereotypes and tagged values are also defined to support WSML. Therefore, the structure aspects of the SWS are modeled with a class diagram based on stereotypes that are defined in the UML profile. The logical and behavioral aspects of the SWS are modeled and expressed using activity diagrams, sequence diagrams, and Object Constraint Language (OCL). Experimental results show that the proposed approach not only increases the level of independence, accuracy, expressiveness, understandability, and machine processability but also significantly reduces the complexity and heterogeneity. Moreover, the proposed method is also evaluated in comparison with the previous methods of the literature, and the results will show that the proposed method outperforms the other methods in terms of expressiveness, understandability, scalability, level of independence, and accuracy.

Managing bias and unfairness in data for decision support: a survey of machine learning and data engineering approaches to identify and mitigate bias and unfairness within data management and analytics systems

The increasing use of data-driven decision support systems in industry and governments is accompanied by the discovery of a plethora of bias and unfairness issues in the outputs of these systems. Multiple computer science communities, and especially machine learning, have started to tackle this problem, often developing algorithmic solutions to mitigate biases to obtain fairer outputs. However, one of the core underlying causes for unfairness is bias in training data which is not fully covered by such approaches. Especially, bias in data is not yet a central topic in data engineering and management research. We survey research on bias and unfairness in several computer science domains, distinguishing between data management publications and other domains. This covers the creation of fairness metrics, fairness identification, and mitigation methods, software engineering approaches and biases in crowdsourcing activities. We identify relevant research gaps and show which data management activities could be repurposed to handle biases and which ones might reinforce such biases. In the second part, we argue for a novel data-centered approach overcoming the limitations of current algorithmic-centered methods. This approach focuses on eliciting and enforcing fairness requirements and constraints on data that systems are trained, validated, and used on. We argue for the need to extend database management systems to handle such constraints and mitigation methods. We discuss the associated future research directions regarding algorithms, formalization, modelling, users, and systems.

Motivations, challenges, and process support for the evolution of existing software to mobile computing platforms

Software maintenance and evolution support changes in the structure and behavior of existing software to change it as per the needs and demands of new requirements. The majority of the existing software systems lack features of mobile computing such as portability, context-awareness, connectivity, and high interactivity. The evolution of the existing software for mobile computing platforms can enable these systems to retain their core data and logic while acquiring new features that are compatible with mobile systems. The objectives of this research are to (i) systematically identify the motivations and challenges of software evolution for mobile computing, and (ii) develop and validate a process model that supports the evolution of existing software to a mobile computing platform. To conduct this research, an empirical software engineering approach has been adopted to investigate existing solutions (30 published studies from 1996 to 2019) and empirically derive a process model that supports software evolution for mobile computing. A case study-based approach is adopted to demonstrate the process-centric evolution of existing software as a mobile-enabled application. Case study-based demonstration highlights that the proposed process (i) supports an incremental evolution and (ii) allows user-decision support to guide the evolution process. Evaluation results highlight computation and energy efficiency along with enhanced usability of a mobile application when executed on resource-constrained mobile devices. The results of this research could help researchers and practitioners to rationalize motivations and challenges to utilize a process-based approach to evolve existing or aging software for mobile computing platforms. Future research is focused on providing patterns and tool support to automate and customize the evolution process.

WindFlow: High-Speed Continuous Stream Processing With Parallel Building Blocks

Nowadays, we are witnessing the diffusion of Stream Processing Systems (SPSs) able to analyze data streams in near realtime. Traditional SPSs like <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Storm</small> and <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Flink</small> target distributed clusters and adopt the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">continuous streaming model</i> , where inputs are processed as soon as they are available while outputs are continuously emitted. Recently, there has been a great focus on SPSs for scale-up machines. Some of them (e.g., <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BriskStream</small> ) still use the continuous model to achieve low latency. Others optimize throughput with batching approaches that are, however, often inadequate to minimize latency for live-streaming applications. Our contribution is to show a novel software engineering approach to design the runtime system of SPSs targeting multicores, with the aim of providing a uniform solution able to optimize throughput and latency. The approach has a formal nature based on the assembly of components called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">building blocks</i> , whose composition allows optimizations to be easily expressed in a compositional manner. We use this methodology to build a new SPS called <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WindFlow</small> . Our evaluation showcases the benefits of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WindFlow</small> : it provides lower latency than SPSs for continuous streaming, and can be configured to optimize throughput, to perform similarly and even better than batch-based scale-up SPSs.

A lightweight virtualization model to enable edge computing in deeply embedded systems

AbstractEdge computing paradigm enables moving Internet of Things (IoT) applications from the Cloud to the edge of the network. Modern software engineering approaches are adhering to microservices to enable the deployment of such applications on edge devices. Microservices consist of the disaggregation of an application into smaller pieces that operate independently. Recent works have explored microservices packaged into containers and advocate that containers result in a reduced footprint and avoid the unwanted overhead caused by traditional virtualization. However, containers cannot be used in many deeply embedded systems (DES) due to an underlying operating system's (OSs) requirement. DES are edge devices with minimal resources regarding storage, memory, and processing power. Thus, they cannot afford large and sophisticated OSs. This article presents the Hellfire hypervisor, a lightweight virtualization implementation that enables separation and improves security in IoT applications on DES. Our proposal simplifies the traditional hypervisor approach and reaches devices where the existing techniques fail. The results show that the proposed model has a small footprint of 23 KB while keeping a low average virtualization overhead of 0.62% for multiple virtual machines execution.

Multi-objective software performance optimisation at the architecture level using randomised search rules

Architecture-based software performance optimisation can help to find potential performance problems and mitigate their negative effects at an early stage. To automate this optimisation process, rule-based and metaheuristic-based performance optimisation methods have been proposed. However, existing rule-based methods explore a limited search space, potentially excluding optimal or near-optimal solutions. Most of current metaheuristic-based methods ignore existing practical knowledge of performance improvement, and lead to solutions that are not easily explicable to humans. To address these problems, we propose a novel approach for performance optimisation at the software architecture level named Multiobjective performance Optimisation based on Randomised search rulEs (MORE). First, we design randomised search rules (MORE-R) to provide explanation without parameters while benefiting from existing practical knowledge of performance improvement. Second, based on all possible composite applications of MORE-R, an explicable multi-objective optimisation problem (MORE-P) is defined to enlarge search space and enable solutions explicable to architectural stakeholder. Third, a multi-objective evolutionary algorithm (MORE-EA) with an introduced do-nothing rule, innovative encoding and repair mechanism is designed to effectively solve MORE-P. The experiments show that MORE is able to achieve more explicable and higher quality solutions than two state-of-the-art techniques. They also demonstrate the benefits of integrating search-based software engineering approaches with practical knowledge.