arXiv subjects

Tommi Mikkonen

Publications and source records attributed to Tommi Mikkonen.

At least 73 records · Page 4Linked to original sources

arXiv2023

Engineering Software Systems for Quantum Computing as a Service: A Mapping Study

Quantum systems have started to emerge as a disruptive technology and enabling platforms - exploiting the principles of quantum mechanics - to achieve quantum supremacy in computing. Academic research, industrial projects (e.g., Amazon Braket), and consortiums like 'Quantum Flagship' are striving to develop practically capable and commercially viable quantum computing (QC) systems and technologies. Quantum Computing as a Service (QCaaS) is viewed as a solution attuned to the philosophy of service-orientation that can offer QC resources and platforms, as utility computing, to individuals and organisations who do not own quantum computers. To understand the quantum service development life cycle and pinpoint emerging trends, we used evidence-based software engineering approach to conduct a systematic mapping study (SMS) of research that enables or enhances QCaaS. The SMS process retrieved a total of 55 studies, and based on their qualitative assessment we selected 9 of them to investigate (i) the functional aspects, design models, patterns, programming languages, deployment platforms, and (ii) trends of emerging research on QCaaS. The results indicate three modelling notations and a catalogue of five design patterns to architect QCaaS, whereas Python (native code or frameworks) and Amazon Braket are the predominant solutions to implement and deploy QCaaS solutions. From the quantum software engineering (QSE) perspective, this SMS provides empirically grounded findings that could help derive processes, patterns, and reference architectures to engineer software services for QC.

cs.SE↗

arXiv2023

Software Architecture for Quantum Computing Systems -- A Systematic Review

Quantum computing systems rely on the principles of quantum mechanics to perform a multitude of computationally challenging tasks more efficiently than their classical counterparts. The architecture of software-intensive systems can empower architects who can leverage architecture-centric processes, practices, description languages, etc., to model, develop, and evolve quantum computing software (quantum software for short) at higher abstraction levels. We conducted a systematic literature review (SLR) to investigate (i) architectural process, (ii) modeling notations, (iii) architecture design patterns, (iv) tool support, and (iv) challenging factors for quantum software architecture. Results of the SLR indicate that quantum software represents a new genre of software-intensive systems; however, existing processes and notations can be tailored to derive the architecting activities and develop modeling languages for quantum software. Quantum bits (Qubits) mapped to Quantum gates (Qugates) can be represented as architectural components and connectors that implement quantum software. Tool-chains can incorporate reusable knowledge and human roles (e.g., quantum domain engineers, quantum code developers) to automate and customize the architectural process. Results of this SLR can facilitate researchers and practitioners to develop new hypotheses to be tested, derive reference architectures, and leverage architecture-centric principles and practices to engineer emerging and next generations of quantum software.

cs.SE↗

arXiv2023

Towards Human-Bot Collaborative Software Architecting with ChatGPT

Architecting software-intensive systems can be a complex process. It deals with the daunting tasks of unifying stakeholders' perspectives, designers' intellect, tool-based automation, pattern-driven reuse, and so on, to sketch a blueprint that guides software implementation and evaluation. Despite its benefits, architecture-centric software engineering (ACSE) inherits a multitude of challenges. ACSE challenges could stem from a lack of standardized processes, socio-technical limitations, and scarcity of human expertise etc. that can impede the development of existing and emergent classes of software (e.g., IoTs, blockchain, quantum systems). Software Development Bots (DevBots) trained on large language models can help synergise architects' knowledge with artificially intelligent decision support to enable rapid architecting in a human-bot collaborative ACSE. An emerging solution to enable this collaboration is ChatGPT, a disruptive technology not primarily introduced for software engineering, but is capable of articulating and refining architectural artifacts based on natural language processing. We detail a case study that involves collaboration between a novice software architect and ChatGPT for architectural analysis, synthesis, and evaluation of a services-driven software application. Preliminary results indicate that ChatGPT can mimic an architect's role to support and often lead ACSE, however; it requires human oversight and decision support for collaborative architecting. Future research focuses on harnessing empirical evidence about architects' productivity and exploring socio-technical aspects of architecting with ChatGPT to tackle emerging and futuristic challenges of ACSE.

cs.SE↗

arXiv2022

The Role of In-House Procurement According to Finnish Municipalities' Purchase Invoice Data

Public sector is a large consumer of ICT systems and services, used for various public services. Tendering for such systems is governed by laws aimed at eliminating unfair advantages and offering all possible parties equal opportunities to participate in the tendering process. In this article, we study in-house rpocurement, where the acquiring organization is an owner of the subcontractor that delivers the system or the service. Municipalities' purchase invoice data is used to determine how much municipalities in Finland depend on in-house procurement. In conclusion, the understanding if included municipalities have ICT service and development units within the organizations needs closer examination, as in-house companies may offer municipalities with limited resources divided costs in the public procurement process.

cs.CY↗

arXiv2022

Agile Practices for Quantum Software Development: Practitioners Perspectives

Quantum software systems are emerging software engineering (SE) genre that exploit principles of quantum bits (Qubit) and quantum gates (Qgates) to solve complex computing problems that today classic computers can not effectively do in a reasonable time. According to its proponents, agile software development practices have the potential to address many of the problems endemic to the development of quantum software. However, there is a dearth of evidence confirming if agile practices suit and can be adopted by software teams as they are in the context of quantum software development. To address this lack, we conducted an empirical study to investigate the needs and challenges of using agile practices to develop quantum software. While our semi-structured interviews with 26 practitioners across 10 countries highlighted the applicability of agile practices in this domain, the interview findings also revealed new challenges impeding the effective incorporation of these practices. Our research findings provide a springboard for further contextualization and seamless integration of agile practices with developing the next generation of quantum software.

cs.SE↗

arXiv2022

Continuous Design Control for Machine Learning in Certified Medical Systems

Continuous software engineering has become commonplace in numerous fields. However, in regulating intensive sectors, where additional concerns needs to be taken into account, it is often considered difficult to apply continuous development approaches, such as devops. In this paper, we present an approach for using pull requests as design controls, and apply this approach to machine learning in certified medical systems leveraging model cards, a novel technique developed to add explainability to machine learning systems, as a regulatory audit trail. The approach is demonstrated with an industrial system that we have used previously to show how medical systems can be developed in a continuous fashion.

cs.SE↗

arXiv2022

Sensitive multi-species photoacoustic gas detection based on mid-infrared supercontinuum source and miniature multipass cell

We report multipass broadband photoacoustic spectroscopy of trace gases in the mid-infrared. The measurement principle of the sensor relies on supercontinuum-based Fourier transform photoacoustic spectroscopy (FT-PAS), in which a scanning interferometer modulates the intensity of a mid-infrared supercontinuum light source and a cantilever microphone is employed for sensitive photoacoustic detection. With a custom-built external Herriott cell, the supercontinuum beam propagates ten times through a miniature and acoustically non-resonant gas cell. The performance of the FT-PAS system is demonstrated by measuring the fundamental C-H stretch bands of various hydrocarbons. A noise equivalent detection limit of 11 ppb is obtained for methane (40 s averaging time, 15 $μ$W/cm$^{-1}$ incident power spectral density, 4 cm$^{-1}$ resolution), which is an improvement by a factor of 12 compared to the best previous FT-PAS systems. High linearity and good stability of the sensor provide reliable identification of individual species from a gas mixture with strong spectral overlap, laying the foundation for sensitive and selective multi-species detection in small sample volumes.

physics.ins-det↗

arXiv2022

Work-from-home and its implication for project management, resilience and innovation -- a global survey on software companies

[Context] The COVID-19 pandemic has had a disruptive impact on how people work and collaborate across all global economic sectors, including the software business. While remote working is not new for software engineers, forced Work-from-home situations to come with both constraints, limitations, and opportunities for individuals, software teams and software companies. As the "new normal" for working might be based on the current state of Work From Home (WFH), it is useful to understand what has happened and learn from that. [Objective] The goal of this study is to gain insights on how their WFH environment impacts software projects and software companies. We are also interested in understanding if the impact differs between software startups and established companies. [Method] We conducted a global-scale, cross-sectional survey during spring and summer 2021. Our results are based on quantitative and qualitative analysis of 297 valid responses. [Results] We observed a mixed perception of the impact of WFH on software project management, resilience, and innovation. Certain patterns on WFH, control and coordination mechanisms and collaborative tools are observed globally. We find that team, agility and leadership are the three most important factors for achieving resilience during the pandemic. Although startups do not perceive the impact of WFH differently, there is a difference between engineers who work in a small team context and those who work in a large team context. [Conclusion] The result suggests a contingency approach in studying and improving WFH practices and environment in the future software industry.

cs.SE↗

arXiv2021

Introducing Traceability in GitHub for Medical Software Development

Assuring traceability from requirements to implementation is a key element when developing safety critical software systems. Traditionally, this traceability is ensured by a waterfall-like process, where phases follow each other, and tracing between different phases can be managed. However, new software development paradigms, such as continuous software engineering and DevOps, which encourage a steady stream of new features, committed by developers in a seemingly uncontrolled fashion in terms of former phasing, challenge this view. In this paper, we introduce our approach that adds traceability capabilities to GitHub, so that the developers can act like they normally do in GitHub context but produce the documentation needed by the regulatory purposes in the process.

cs.SE↗

arXiv2021

On Misbehaviour and Fault Tolerance in Machine Learning Systems

Machine learning (ML) provides us with numerous opportunities, allowing ML systems to adapt to new situations and contexts. At the same time, this adaptability raises uncertainties concerning the run-time product quality or dependability, such as reliability and security, of these systems. Systems can be tested and monitored, but this does not provide protection against faults and failures in adapted ML systems themselves. We studied software designs that aim at introducing fault tolerance in ML systems so that possible problems in ML components of the systems can be avoided. The research was conducted as a case study, and its data was collected through five semi-structured interviews with experienced software architects. We present a conceptualisation of the misbehaviour of ML systems, the perceived role of fault tolerance, and the designs used. Common patterns to incorporating ML components in design in a fault tolerant fashion have started to emerge. ML models are, for example, guarded by monitoring the inputs and their distribution, and enforcing business rules on acceptable outputs. Multiple, specialised ML models are used to adapt to the variations and changes in the surrounding world, and simpler fall-over techniques like default outputs are put in place to have systems up and running in the face of problems. However, the general role of these patterns is not widely acknowledged. This is mainly due to the relative immaturity of using ML as part of a complete software system: the field still lacks established frameworks and practices beyond training to implement, operate, and maintain the software that utilises ML. ML software engineering needs further analysis and development on all fronts.

cs.SE↗

arXiv2021

Systematic Literature Review of Validation Methods for AI Systems

Context: Artificial intelligence (AI) has made its way into everyday activities, particularly through new techniques such as machine learning (ML). These techniques are implementable with little domain knowledge. This, combined with the difficulty of testing AI systems with traditional methods, has made system trustworthiness a pressing issue. Objective: This paper studies the methods used to validate practical AI systems reported in the literature. Our goal is to classify and describe the methods that are used in realistic settings to ensure the dependability of AI systems. Method: A systematic literature review resulted in 90 papers. Systems presented in the papers were analysed based on their domain, task, complexity, and applied validation methods. Results: The validation methods were synthesized into a taxonomy consisting of trial, simulation, model-centred validation, and expert opinion. Failure monitors, safety channels, redundancy, voting, and input and output restrictions are methods used to continuously validate the systems after deployment. Conclusions: Our results clarify existing strategies applied to validation. They form a basis for the synthesization, assessment, and refinement of AI system validation in research and guidelines for validating individual systems in practice. While various validation strategies have all been relatively widely applied, only few studies report on continuous validation. Keywords: artificial intelligence, machine learning, validation, testing, V&V, systematic literature review.

cs.SE↗

arXiv2021

Personal Data Gentrification

We live in an era in which the most valued services are not paid for in money, but in personal data. Every day, service providers collect the personal information of billions of individuals, information that sustain their infrastructure by marketing profiles labeled with this information to personal data consumers, such as advertisers. Not all uses of this personal data are for marketing; data consumers can also include, for instance, public health authorities tracking pandemics. In either case, individuals have undergone a process of Personal Data Gentrification, as data ownership has shifted from individuals to service providers and data consumers, as if the data is worth nothing to the individuals; these new owners then harness the data to obtain large profits. Current privacy-enhancing technologies are beginning to allow individuals to control and share less information. However, not sharing individuals' personal information at all could lead to Personal Data Blight, in which the potential of personal data in applications that benefit all of society remains forever latent. In this paper, we propose Personal Data Enfranchisement as a middle ground, empowering individuals to control the sharing of their personal information to shift the business flows of personal information. Based on these insights, we propose a model to gradually and incrementally make a shift from our current situation towards one of Personal Data Enfranchisement. Finally, we present a roadmap and some challenges towards achieving this bold vision.

cs.CY↗

arXiv2021

Experiences on Managing Technical Debt with Code Smells and AntiPatterns

Technical debt has become a common metaphor for the accumulation of software design and implementation choices that seek fast initial gains but that are under par and counterproductive in the long run. However, as a metaphor, technical debt does not offer actionable advice on how to get rid of it. To get to a practical level in solving problems, more focused mechanisms are needed. Commonly used approaches for this include identifying code smells as quick indications of possible problems in the codebase and detecting the presence of AntiPatterns that refer to overt, recurring problems in design. There are known remedies for both code smells and AntiPatterns. In paper, our goal is to show how to effectively use common tools and the existing body of knowledge on code smells and AntiPatterns to detect technical debt and pay it back. We present two main results: (i) How a combination of static code analysis and manual inspection was used to detect code smells in a codebase leading to the discovery of AntiPatterns; and (ii) How AntiPatterns were used to identify, characterize, and fix problems in the software. The experiences stem from a private company and its long-lasting software product development effort.

cs.SE↗

arXiv2021

Extending SOUP to ML Models When DesigningCertified Medical Systems

Software of Unknown Provenance, SOUP, refers to a software component that is already developed and widely available from a 3rd party, and that has not been developed, to be integrated into a medical device. From regulatory perspective, SOUP software requires special considerations, as the developers' obligations related to design and implementation are not applied to it. In this paper, we consider the implications of extending the concept of SOUP to machine learning (ML) models. As the contribution, we propose practical means to manage the added complexity of 3rd party ML models in regulated development.

cs.SE↗

arXiv2021

MLOps Challenges in Multi-Organization Setup: Experiences from Two Real-World Cases

The emerging age of connected, digital world means that there are tons of data, distributed to various organizations and their databases. Since this data can be confidential in nature, it cannot always be openly shared in seek of artificial intelligence (AI) and machine learning (ML) solutions. Instead, we need integration mechanisms, analogous to integration patterns in information systems, to create multi-organization AI/ML systems. In this paper, we present two real-world cases. First, we study integration between two organizations in detail. Second, we address scaling of AI/ML to multi-organization context. The setup we assume is that of continuous deployment, often referred to DevOps in software development. When also ML components are deployed in a similar fashion, term MLOps is used. Towards the end of the paper, we list the main observations and draw some final conclusions. Finally, we propose some directions for future work.

cs.SE↗

arXiv2021

Who Needs MLOps: What Data Scientists Seek to Accomplish and How Can MLOps Help?

Following continuous software engineering practices, there has been an increasing interest in rapid deployment of machine learning (ML) features, called MLOps. In this paper, we study the importance of MLOps in the context of data scientists' daily activities, based on a survey where we collected responses from 331 professionals from 63 different countries in ML domain, indicating on what they were working on in the last three months. Based on the results, up to 40% respondents say that they work with both models and infrastructure; the majority of the work revolves around relational and time series data; and the largest categories of problems to be solved are predictive analysis, time series data, and computer vision. The biggest perceived problems revolve around data, although there is some awareness of problems related to deploying models to production and related procedures. To hypothesise, we believe that organisations represented in the survey can be divided to three categories -- (i) figuring out how to best use data; (ii) focusing on building the first models and getting them to production; and (iii) managing several models, their versions and training datasets, as well as retraining and frequent deployment of retrained models. In the results, the majority of respondents are in category (i) or (ii), focusing on data and models; however the benefits of MLOps only emerge in category (iii) when there is a need for frequent retraining and redeployment. Hence, setting up an MLOps pipeline is a natural step to take, when an organization takes the step from ML as a proof-of-concept to ML as a part of nominal activities.

cs.SE↗

arXiv2021

Challenges and Governance Solutions for Data Science Services based on Open Data and APIs

Increasingly common open data and open application programming interfaces (APIs) together with the progress of data science -- such as artificial intelligence (AI) and especially machine learning (ML) -- create opportunities to build novel services by combining data from different sources. In this experience report, we describe our firsthand experiences on open data and in the domain of marine traffic in Finland and Sweden and identified technological opportunities for novel services. We enumerate five challenges that we have encountered with the application of open data: relevant data, historical data, licensing, runtime quality, and API evolution. These challenges affect both business model and technical implementation. We discuss how these challenges could be alleviated by better governance practices for provided open APIs and data.

cs.SE↗

arXiv2021

On Medical Device Cybersecurity Compliance in EU

The medical device products at the European Union market must be safe and effective. To ensure this, medical device manufacturers must comply to the new regulatory requirements brought by the Medical Device Regulation (MDR) and the In Vitro Diagnostic Medical Device Regulation (IVDR). In general, the new regulations increase regulatory requirements and oversight, especially for medical software, and this is also true for requirements related to cybersecurity, which are now explicitly addressed in the legislation. The significant legislation changes currently underway, combined with increased cybersecurity requirements, create unique challenges for manufacturers to comply with the regulatory framework. In this paper, we review the new cybersecurity requirements in the light of currently available guidance documents, and pinpoint four core concepts around which cybersecurity compliance can be built. We argue that these core concepts form a foundations for cybersecurity compliance in the European Union regulatory framework.

cs.CR↗