Felicien Ihirwe - publications

2024

"A Rule-Based Log Analysis Approach for State-Machine Governed Systems. By Jeroen Zwysen, Felicien Ihirwe, Ken Vanherpen1, Maarten Vergouwe, Umut Caliskan and Davy Maes. 26th International Conference on Enterprise Information Systems.Volume 2: ICEIS; ISBN 978-989-758-692-7; ISSN 2184-4992, SciTePress, pages 77-88, Angers, France DOI: https://doi.org/10.5220/0012702300003690
Abstract Full text
Logs are used in programming for various purposes, ranging from failure analysis to software comprehension. However, the processing of logs is hindered by the lack of structure in the logs, the required domain knowledge for interpretation, and a lack of tooling. In this paper, a novel approach that includes structured log generation and rule-based log analysis is presented. Targeting state machine-governed systems, the approach relies on developers' knowledge during design time to allow hierarchical grouping of logs and standard visualization of the logs during the analysis. This allows automated failure diagnosis and localization without full system-wide domain knowledge as well as providing a historical context of the system during a failure event. To better evaluate the effectiveness of the approach, two use cases, namely a Virtual Coffee Machine (VCM) and an Automated Mobile Robot (AMR) are showcased and analyzed.
"CHESSIoT: A model-driven approach for engineering multi-layered IoT systems". By Felicien Ihirwe, Davide Di Ruscio, Simone Gianfranceschi, and Alfonso Pierantonio. Journal of Computer Languages. Volume 78, ISSN 2590-1184, March 2024, DOI: https://doi.org/10.1016/j.cola.2023.101254
Abstract Full text
Context: The current technology revolution, which places the highest value on people’s welfare, is frequently seen as being mainly supported by Internet of Things (IoT) technologies. IoT is regarded as a powerful multi-layered network of systems that integrates several heterogeneous, independently networked (sub-)systems working together to achieve a shared purpose.
Objective: In this article, we present CHESSIoT, a model-driven engineering environment that integrates high-level visual design languages, software development, safety analysis, and deployment approaches for engineering multi-layered IoT systems. With CHESSIoT, users may conduct different engineering tasks on system and software models under development to enable earlier decision-making and take prospective measures, all supported by a unique environment.
Methodology:This is achieved through multi-staged designs, most notably the physical, functional, and deployment architectures. The physical model specification is used to perform both qualitative and quantitative safety analysis by employing logical Fault-Trees models (FTs). The functional model specifies the system’s functional behavior and is later used to generate platform-specific code that can be deployed on low-level IoT device nodes. Additionally, the framework supports modeling the system’s deployment plan and run-time service provisioning, which would ultimately be transformed into deployment configuration artifacts ready for execution on remote servers.
Results: To showcase the effectiveness of our proposed approach, as well as the capability of the supporting tool, a multi-layered Home Automation system (HAS) scenario has been developed covering all its design, development, analysis, and deployment aspects. Furthermore, we present the results from different evaluation mechanisms which include a comparative analysis and a qualitative assessment. The evaluation mechanisms target mainly completeness of CHESSIoT by addressing specific research questions.
"Supporting the model-based safety analysis for safety-critical IoT systems". By Felicien Ihirwe, Davide Di Ruscio, Katia Di Blasio, Simone Gianfranceschi, and Alfonso Pierantonio. Journal of Computer Languages. Volume 78, ISSN 2590-1184, March 2024, DOI: https://doi.org/10.1016/j.cola.2023.101243
Abstract Full text
Dependability is regarded as the ability of the system to provide services that can be trusted within a specific period. As the complexity and heterogeneity of Internet of Things (IoT) systems rise, so does the possibility of errors and failure. Early safety analysis not only reduces the cost of late failure but also makes it easier to trace and determine the source of the failure beforehand in case something goes wrong. In this paper, we present an early safety analysis approach based on Failure-Logic Analysis (FLA) and Fault-Tree Analysis (FTA) for safety-critical IoT systems. The safety analysis infrastructure, supported by the CHESSIoT tool, takes into account the system-level physical architecture model annotated with the component’s failure logic properties to perform different kinds of automated failure analyses. In addition to its ability to generate the system Fault-Trees (FTs), the new FTA analysis approach automatically performs qualitative and quantitative analyses which include the elimination of redundant events, unnecessary failure paths, as well as automatic probabilistic calculation of the undesired events. To assess the effectiveness of the approach, a comparative study between our propose approach with 19 existing approaches in both academia and industry was conducted showcasing its contribution to the state of the art. Finally, a Patient Monitoring System (PMS) use case has been developed to demonstrate the capabilities of the supporting CHESSIoT tool, and the results are thoroughly presented.
"Towards a pre-trained Question-Answering language model for Kinyarwanda". By Diane Tuyizere, Felicien Ihirwe, Remy Ihabwikuzo, Guda Blessed, Edith Luhanga. Accepted for publication: 3rd IEEE International Conference on Signal, Control and Communication (SCC 2023). Hammamet, Tunisia, on 1st – 3rd December 2023.
Abstract Full text
Kinyarwanda, as one of the official languages of Rwanda plays a significant role in the country's cultural, educational, and administrative domains. With the increasing demand for information retrieval and natural language understanding in Kinyarwanda, the development of robust Question Answering (QA) systems becomes imperative. Pretrained Language Models (PLMs) have demonstrated remarkable success in various natural language processing tasks, but their potential to address the specific challenges of Kinyarwanda QA remains largely unexplored. In this paper, we present a novel and comprehensive investigation into the feasibility and effectiveness of utilizing PLMs to tackle question-answering tasks for the Kinyarwanda language. We began by curating the annotated large-scale dataset of Kinyarwanda questions and corresponding answers, specifically tailored to the nuances of the language. The datasets utilized were from the sports and entertainment domain collected from several Kinyarwanda newspapers. Our approach relied on existing popular and robust Multi-Lingual language models. We developed various improved language models specific to Kinyarwanda QA tasks based on different models such as BERT, DistilBERT, and Roberta. We assessed their performance on question understanding, context comprehension, and answer generation. Our findings have shown that the pre-trained language models exhibit promising potential in advancing Kinyarwanda QA with an F1 score of 50.4%.

2023

THESIS: "Low-code Engineering for the Internet of Things". By Felicien Ihirwe. Doctorate Thesis July 2023 Supervisors: Davide Di Ruscio, Alfonso Pierantonio, and Simone Gianfranceschi. Available at: https://dx.doi.org/10.2139/ssrn.4539001.
Abstract Full text
The Internet of Things (IoT) technologies are often seen as the main drivers of the current technological revolution, which devotes the most priority to improving the well-being of humanity. IoT is typically regarded as a powerful network of systems that integrates several heterogeneous and independently networked devices working together to achieve a shared purpose. Engineering such systems requires efficient tools to deal with the intrinsic complexities while offering means to increase system reliability by limiting future repair costs. Low-Code Development Platforms (LCDPs) unravel the opportunities to advance the simplicity of how new applications are developed in different business application domains. However, in the IoT domain, systems are complex, multi-layered, and highly heterogeneous in all aspects, not to mention the large amount of data being collected and processed concurrently. Even though there is a convenient push toward coping with such complexities, there still needs to be a massive gap regarding the actual development techniques that support early system analysis, deployment, and run-time management. Low-Code Engineering (LCE), on the other hand, aims to tackle such issues by extending the development knowledge present in LCDPs to a more sophisticated era of "Low-Code Engineering Platforms (LCEPs)" by injecting into it the theoretical and technical concepts present in Model-Driven Engineering (MDE), Cloud Computing, and Machine Learning. These platforms target more sophisticated domains such as IoT, industrial automation, data science, recommender systems, etc. This dissertation addresses such challenges by first presenting the current state of the art of Low-Code Engineering Platforms (LCEPs), which gives a better understanding of what LCEPs are and their differences with respect to existing LCDPs, particularly in the IoT domain. We also highlight how MDE plays a significant role in the LCE's evolution. Then, we examine the current limitations, open challenges, and opportunities of existing IoT Engineering platforms in realizing such an initiative. While evaluating the quality of such complex platforms could be challenging, we propose the software product quality model for evaluating the static and dynamic quality properties of such engineering platforms.

The complexity behind the automated realization of IoT systems can be extremely daunting. One efficient approach is to adopt Domain-Specific Languages (DSLs). DSLs are tailored to the specific domain to pave the way for the domain experts to define the system's behavior based on their expertise. This dissertation presents CHESSIoT, a platform that integrates high-level visual DSLs, software development, safety analysis, and deployment mechanisms for engineering multi-layered IoT systems. With CHESSIoT, users may conduct various engineering tasks on system and software models to enable earlier decision-making. This is achieved in a unique environment that combines multi-staged designs, most notably the system-level, functional, and deployment architectures. The physical architecture specifically contains the high-level system building blocks and their interconnections suitable to perform both early qualitative and quantitative safety analysis by employing logical Fault-Trees (FTs). On the other hand, the software model is equipped with the system's functional behavior suitable for generating platform-specific code ready to be deployed on low-level IoT device nodes. Additionally, the framework supports modeling of the system's deployment, which would ultimately be used to generate deployment artifacts. To facilitate run-time management of deployed services, the tool offers means for defining run-time service provisioning modules through which deployment rules are defined and configured. To demonstrate the effectiveness of our proposed approach, throughout this dissertation, different comparative assessment was conducted to highlight the potential contribution of our approach in relation to existing approaches. Finally, we used the implications from the conducted research studies as well as experiments from running examples to tackle potential research questions as well as demonstrate the capabilities of our supporting tool.
"Supporting Early-Safety Analysis of IoT Systems by Exploiting Testing Techniques". By Diego Clerissi, Juri Di Rocco, Davide Di Ruscio, Claudio Di Sipio, Felicien Ihirwe, Leonardo Mariani, Daniela Micucci, Maria Teresa Rossi Riccardo Rubei. In Proceedings of the 26rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings (MODELS'23). October 2023
Abstract Full text
IoT systems' complexity and susceptibility to failures pose significant challenges in ensuring their reliable operation. Failures can be internally generated or caused by external factors, impacting both the system's correctness and its surrounding environment. To investigate these complexities, various modeling approaches have been proposed to raise the level of abstraction, facilitating automation and analysis. Failure-Logic Analysis (FLA) is a technique that helps predict potential failure scenarios by defining how a component's failure logic behaves and spreads throughout the system. However, manually specifying FLA rules can be arduous and error-prone, leading to incomplete or inaccurate specifications. In this paper, we propose adopting testing methodologies to improve the completeness and correctness of these rules. How failures may propagate within an IoT system can be observed by systematically injecting failures, while running test cases to collect evidence useful to add, complete and refine FLA rules.
"Comparison of Tree-based Machine Learning Algorithms to Predict Reporting Behavior of Electronic Billing Machines". By Belle Fille Murorunkwere Felicien Ihirwe , Idrissa Kayijuka, Joseph Nzabanita, and Dominique Haughton. Journal: Information. February 2023, Paper:140, Vol:14, Issue:3 DOI:10.3390/info14030140 [Online]
Abstract Full text

Tax fraud is a common problem for many tax administrations, costing billions of dollars. Different tax administrations have considered several options to optimize revenue; among them, there is the so-called electronic billing machine (EBM), which aims to monitor all business transactions and, as a result, boost value added tax (VAT) revenue and compliance. Most of the current research has focused on the impact of EBMs on VAT revenue collection and compliance rather than understanding how EBM reporting behavior influences future compliance. The essential contribution of this study is that it leverages both EBM’s historical reporting behavior and actual business characteristics to understand and predict the future reporting behavior of EBMs. Herein, tree-based machine learning algorithms such as decision trees, random forest, gradient boost, and XGBoost are utilized, tested, and compared for better performance. The results exhibit the robustness of the random forest model, among others, with an accuracy of 92.3%. This paper clearly presents our approach contribution with respect to existing approaches through well-defined research questions, analysis mechanisms, and constructive discussions. Once applied, we believe that our approach could ultimately help the tax-collecting agency conduct timely interventions on EBM compliance, which will help achieve the EBM objective of improving VAT compliance.

2022

"Assessing the Quality of Low-Code and Model-driven Engineering Platforms for Engineering IoT Systems". By Felicien Ihirwe, Davide Di Ruscio, Simone Gianfranceschi, and Alfonso Pierantonio. In Proceedings of the 22nd IEEE International Conference on Software Quality, Reliability, and Security (QRS22). November 2022 DOI: 10.2139/ssrn.4267269 [Online]
Abstract Full text

Over the last few years, industry and academia have proposed several Low-Code and Model-driven Engineering (MDE) platforms to ease the engineering process of the Internet of things (IoT) systems. However, deciding whether such engineering platforms meet the minimum required software quality standards is not straightforward. Software quality can be defined as the degree to which a software system achieves its intended goal. Various software quality standards have been established to aid in the software quality assessment process; however, due to the nature of engineering IoT platforms, such models may not entirely suit the IoT domain. This paper presents a model for assessing the software quality of Low-Code and MDE platforms for engineering IoT platforms. The proposed software quality model is based on and extends the ISO/IEC 25010:2011 software product quality model standard. It is intended to assist IoT practitioners in assessing and establishing quality requirements for engineering IoT platforms. To determine the effectiveness of the proposed model, we used it to evaluate the quality of 17 IoT engineering platforms, and the results obtained are promising.

2021

"Cloud-based modeling in IoT domain: a survey, open challenges and opportunities". By Felicien Ihirwe, Arsene Indamutsa, Davide Di Ruscio, Silvia Mazzini, and Alfonso Pierantonio. In the Proceedings of 2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C) DOI: 10.1109/MODELS-C53483.2021.00018
Abstract Full text

The current evolution of cloud-based computing opens up a lot of possibilities for software development. In the near future, complex systems in various domains such as Space, Automotive, Internet of Things, and Smart Cities, will be designed, developed, and deployed from cloud-based environments, hence lowering production and maintenance costs. However, in the IoT domain, parts of the system have to run on Edge, Fog, or Cloud, posing significant difficulties in determining what, where, and when to develop. Therefore, this paper conducted a thorough review to investigate where the IoT domain community stands concerning the current trend of moving traditional modeling infrastructures to the cloud. Following an examination of 625 articles, we focus on 22 different cloud-based IoT system development approaches. Moreover, we highlight various opportunities and challenges related to the adoption of cloud-based modeling tools and platforms in the IoT domain.
"A domain specific modeling and analysis environment for complex IoT applications". By Felicien Ihirwe, Davide Di Ruscio, Silvia Mazzini, and Alfonso Pierantonio. In the 7th Italian Conference on ICT for Smart Cities And Communities (I-CiTies'21). September 2021
Abstract Full text

To cope with the complexities found in the Internet of Things domain, designers and developers of IoT applications demand practical tools. Several model-driven engineering methodologies and tools have been developed to address such difficulties, but few of them address the analysis aspects. In this extended abstract, we introduce CHESSIoT, a domain-specific modeling environment for complex IoT applications. In addition, the existing supported real-time analysis mechanism, as well as a proposed code generation approach, are presented.
"Towards an MQTT5 geo-location extension for location-aware applications". By Felicien Ihirwe, Giovanni Iovino, and Davide Di Ruscio. In the 44th IEEE International Conference on Telecommunications and Signal Processing (TSP'21). July 2021 DOI: 10.1109/TSP52935.2021.9522590
Abstract Full text Video

Location-aware applications are becoming popular nowadays because of the increasing adoption of edge and fog computing. This poses novel difficulties to the conventional applications that utilize the Message Queuing Telemetry Transport (MQTT) as their prevalent communication channel. Also, developers are obliged to figure out the code at whatever point they need to build up MQTT-based location-aware applications. This can causes various issues, for example, protocol standard infringement or problems in data acquisition. This paper proposes a novel extension of the recently released MQTT5 protocol that will permit message delivery by not only respecting the topics of interest but also in addition to geo-referenced information provided by both publishers and subscribers.
"Towards a modeling and analysis environment for industrial IoT systems". By Felicien Ihirwe, Davide Di Ruscio, Silvia Mazzini, and Alfonso Pierantonio. In the International workshop on MDE for Smart IoT Systems co-located with Software Technologies: Applications and Foundations (MESS@STAF21) conferences. June 2021 http://ceur-ws.org/Vol-2999/messpaper1.pdf
Abstract Full text Video

The development of Industrial Internet of Things systems (IIoT) requires tools robust enough to cope with the complexity and heterogeneity of such systems, which are supposed to work in safety-critical conditions. The availability of methodologies to support early analysis, verification, and validation is still an open issue in the research community. The early real-time schedulability analysis can help quantify to what extent the desired system's timing performance can eventually be achieved. In this paper, we present CHESSIoT, a model-driven environment to support the design and analysis of industrial IoT systems. CHESSIoT follows a multi-view, component-based modelling approach with a comprehensive way to perform event-based modelling on system components for code generation purposes employing an intermediate ThingML model. To showcase the capability of the extension, we have designed and analysed an Industrial real-time safety use case.
"Model-based Analysis Support for Dependable Complex Systems in CHESS". By Alberto Debiasi, Felicien Ihirwe, Pierluigi Pierini, Silvia Mazzini, and Stefano Tonetta. In Proceedings of the 9th International Conference on Model-Driven Engineering and Software Development - MODELSWARD'21. February 2021 http://dx.doi.org/10.5220/0010269702620269

Abstract Full text DOI

The challenges related to dependable complex systems are heterogeneous and involve different aspects of the system. On one hand, the decision-making processes need to take into account many options. On the other hand, the design of the system logical architecture must consider various dependability concerns such as safety, reliability, and security. Moreover, in case of high-assurance systems, the analysis of such concerns must be performed with rigorous methods. In this paper, we present the new development of CHESS, a cross-domain, model-driven, component-based and open-source tool for the development of high-integrity systems. We focus on the new recently distributed version of CHESS, which supports extended model-based development and analyses for safety and security concerns.

2020

"Low-code Engineering for Internet of things: A state of research". By Felicien Ihirwe, Davide Di Ruscio, Silvia Mazzini, Pierluigi Pierini, and Alfonso Pierantonio. In Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings (MODELS'20). October 2020 https://dl.acm.org/doi/10.1145/3417990.3420208
Abstract Full text DOI

Developing Internet of Things (IoT) systems has to cope with several challenges mainly because of the heterogeneity of the involved sub-systems and components. With the aim of conceiving languages and tools supporting the development of IoT systems, this paper presents the results of the study, which has been conducted to understand the current state of the art of existing platforms, and in particular low-code ones, for developing IoT systems. By analyzing sixteen platforms, a corresponding set of features has been identified to represent the functionalities and the services that each analyzed platform can support. We also identify the limitations of already existing approaches and discuss possible ways to improve and address them in the future.

↓ In processing papers ↓

"A rule-based logging and analysis approach for systems governed by state machine behavior". By TBD. UNDER REVIEW: 32nd IEEE/ACM International Conference on Program Comprehension (ICPC 2024) 2023.
Abstract
Logs are used in programming for various purposes ranging from issue analysis to software comprehension. However, the processing of logs is hindered by the lack of structure in the data, the required domain knowledge for interpretation, and a lack of tooling. In this paper, a novel methodology for structured log generation and analysis is proposed. The method relies on developers' knowledge during design time and results in logging data being compressed and formatted into a hierarchical view, allowing for fast issue localization without full system-wide domain knowledge of the application. The methodology is aimed towards state machine software applications and implemented for two test cases, each with systems containing a software controller and a hardware plant.
"Low-code Engineering Platforms: a starter perspective". By Felicien Ihirwe, TBD. IN DRAFT 2023.
Abstract
Low-Code/No-Code Development Platforms (LCDP/NCDP) are being seen as a potential future for software development. With the aim at attracting non-technical users who have less of no experience in software development, these platforms promises to drastically reduce or remove entirely the amount of code from the user. Although this is the case, the recent researches have shown that these platforms suffer when it comes to complex domains which involves sophisticated engineering tasks in decision making processes. Low-Code Engineering Platforms (LCEP) on the other hand aim at tackling such gap by talking into account researches in Model-Driven Engineering (MDE), Cloud computing and Machine Learning. However, there is still a huge debate on what such platforms are bringing to the table in what regards to existing platforms in MDE and LCDP. In the paper, we aim at clarifying that by providing concise though on what LCEP are and where they stand in regards to the existing technologies in MDE and LCDP. We will highlights the key differences and intersection between the three and draw a conclusion on the current state in the realization of such platforms.