Alessio Bucaioni

Mälardalen University

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Co-Authors

Saad Mubeen

Mälardalen University

Mikael Sjödin

Mälardalen University

Federico Ciccozzi

Mälardalen University

Antonio Cicchetti

Mälardalen University

Patrizio Pelliccione

Gran Sasso Science Institute

Enxhi Ferko

Mälardalen University

John Lundbäck

Arcticus Systems (Sweden)

Francesco Flammini

University of Applied Sciences and Arts of Southern Switzerland

Mehrdad Saadatmand

RISE Research Institutes of Sweden

Amleto Di Salle

Gran Sasso Science Institute

Cooperative Institutions

Mälardalen University

University of L'Aquila

Gran Sasso Science Institute

Arcticus Systems (Sweden)

University of Waterloo

Chalmers University of Technology

Bombardier (Sweden)

Technology Centre

Software (Spain)

Institut national de recherche en informatique et en automatique

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An Evaluation Framework for Modeling Languages Supporting Predictable Vehicular Software Systems

Advances in intelligent systems and computing (2022)

Enxhi Ferko Igli Jasharllari Alessio Bucaioni Mohammad Ashjaei Saad Mubeen

Modeling language

Predictability

10.1007/978-3-030-97652-1_7

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Delineation Protocol of Agricultural Management Zones (Olive Trees, Alfalfa) at Field Scale (Crete, Greece)

David Chatzidavid Eleni Kokinou Nikolaos Gerarchakis Ioannis Kontogiorgakis Alessio Bucaioni

This study proposes a flexible and adaptable protocol for the establishment of agricultural management zones that utilises remote sensing, ground truthing (apparent electrical conductivity and soil sampling), the IRRIGOPTIMAL® system and machine learning. This protocol could contribute significantly to the rational use of inputs (water, fertilizers and pesticides) and to the further efficient development of an optimal irrigation system with variable irrigation rates. The methodology to develop this protocol was applied to olive and alfalfa plots in Heraklion (Crete, Greece) to monitor soil and crop responses for the period 2022-2024. Spatial and temporal assessment of selected soil and plant parameters (moisture, photosynthetic activity) using ground and vegetation reflectance mapping by satellites and unmanned aerial systems provides important information in both the pre- and main phases of the management zone delineation. Geophysical methods such as electromagnetic induction, applied in the main phase of management zone delineation, provide a robust technique to determine the spatial and temporal distribution of apparent electrical conductivity. The Random Forest machine learning model was found to be most suitable for predicting soil electrical conductivity based on satellite-derived salinity indices and ground electromagnetic induction. Finally, the IRRIGOPTIMAL® system provides real-time monitoring of a variety of weather and soil parameters to determine the optimal cultivation of crops based on the creation of agricultural management zones.

Precision Agriculture

10.20944/preprints202408.1118.v1

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Citations (1)

Technical Credit

Communications of the ACM (2024)

Ian Gorton Alessio Bucaioni Patrizio Pelliccione

Balancing initial investment and long-term results in the software development process.

Investment

10.1145/3690043

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Enabling Automated Integration of Architectural Languages:an Experience Report from the Automotive Domain

Zenodo (CERN European Organization for Nuclear Research) (2021)

Alessio Bucaioni Matthias Becker

Dataset for the journal "Enabling Automated Integration of Architectural Languages:an Experience Report from the Automotive Domain"

Domain-specific language

10.5281/zenodo.5529655

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Modeling of Vehicular Distributed Embedded Systems: Transition from Single-Core to Multi-core

Advances in intelligent systems and computing (2017)

Saad Mubeen Alessio Bucaioni

Component (thermodynamics)

Backward compatibility

Core model

10.1007/978-3-319-54978-1_76

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Raising Abstraction of Timing Analysis through Model-Driven Engineering

Alessio Bucaioni

Raising (metalworking)

Abstraction

Source

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Raising Abstraction in Timing Analysis for Vehicular Embedded Systems through Model-Driven Engineering

Alessio Bucaioni

The complexity of software running on vehicular embedded systems is constantlyincreasing and this negatively affects its development costs and time tomarket. One way to deal with these issues is to boost abstraction in the formof models to (i) ease the reasoning about the system architecture, (ii) automatecertain stages of the development, (iii) early detect flaws in the system architecturethrough fundamental analysis and (iv) take appropriate countermeasuresbefore the system is implemented.Considering the importance of timing requirements in the design of softwarefor vehicular embedded systems, in this licentiate thesis we leverageModel-Driven Engineering for realizing a semi-automatic approach which allowsthe developer to perform end-to-end delay timing analysis on design models,without having to manually model timing elements and set their values.The proposed approach, starting from a design model of an automotivesoftware functionality, automatically generates a set of models enriched withtiming elements whose values are set at generation time. End-to-end delay timinganalysis is run on the generated models and, based on the analysis results,the approach automatically selects the generated models which better meet aspecific set of timing requirements.

Static timing analysis

Abstraction

Systems analysis

Abstraction layer

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Citations (2)

Modeling Centralised Automotive E/E Software Architectures

SSRN Electronic Journal (2022)

Alessio Bucaioni Patrizio Pelliccione Saad Mubeen

The automotive domain is living an exciting period triggered by challenging business and technology drivers, like electrification, autonomous driving, over-the-air software updates and connected vehicles, just to mention a few. This profoundly impacted the electric and electronic automotive architecture and pushed more and more manufacturers to shift towards more centralised electric and electronic architectures for their future automotive software systems. In this work, we first analyse the readiness of four main automotive architectural languages to represent novel vehicle centralised architectures. Based on the analysis results, we propose an extension to one of these languages to fully support the modelling of technical reference architectures for centralised vehicles.We validate the proposed extension using workshops with experts in the automotive domain and using an automotive use case describing an autonomous quarry.

10.2139/ssrn.4085480

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Handling Uncertainty in Automatically Generated Implementation Models in the Automotive Domain

Alessio Bucaioni Antonio Cicchetti Federico Ciccozzi Saad Mubeen Alfonso Pierantonio

Models and model transformations, the two core constituents of Model-Driven Engineering, aid in software development by automating, thus taming, error-proneness of tedious engineering activities. In many cases, the result of these automated activities is an overwhelming amount of information. This is the case of one-to-many model transformations that, e.g. in model-based design-space exploration, can potentially generate a massive amount of candidate models (i.e., solution space) from one single source model. In our scenario, from one design model we generate a set of possible implementation models on which timing analysis is run. The aim is to find the best model from a timing perspective. However, multiple implementation models can have equally good analysis results. Therefore, the engineer is expected to investigate the solution space for making a final decision, using criteria which fall outside the analysis' criteria themselves. Since candidate models can be many and very similar to each other, manually finding differences and commonalities is an impractical and error-prone task. In order to provide the engineer with an expressive representation of models' commonalities and differences, we propose the use of modelling with uncertainty. We achieve this by elevating the solution space to a first-class status, adopting a compact notation capable of representing the solution space by means of a single model with uncertainty. Commonalities and differences are thus represented by means of uncertainty points for the engineer to easily grasp them and consistently make her decision without manually inspecting each model individually.

Representation

Design space exploration

Model Transformation

10.1109/seaa.2016.32

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Citations (13)

Automatic Generation of Configuration Files: an Experience Report from the Railway Domain.

The Journal of Object Technology (2021)

Enxhi Ferko Alessio Bucaioni Jan Carlson Zulqarnain Haider

In recent years, software product line development has been adopted by a growing number of companies.Within software product line development, one way of creating specific products is by using configuration files to control a given set of parameters of the product at run time.Often, configuration files are created manually and this may lead to a sub-optimal process with respect to development effort and error proneness.In this experience report, we describe our work in enabling the automatic generation of configuration files in the railway domain.We discuss a four-step approach whose generation mechanism uses concepts of generative programming.The approach is the outcome of a bottom-up effort leveraging the experiences and the results from our technology transfer activities with our industrial partner, Bombardier Transportation.We evaluate the applicability and the correctness of the proposed approach using the Aventra train family from Bombardier Transportation.Besides, we evaluate the ability of the proposed approach in mitigating the development effort and error proneness typical of traditional manual approaches.We performed expert interviews to assess the industrial relevance of the proposed approach and collect qualitative feedback on the perceived benefits and drawbacks.Eventually, for each of the four steps composing the proposed approach, we identify factors that might affect the adoption of the approach and use these factors for discussing the lessons we have learned.

10.5381/jot.2021.20.3.a4

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Citations (4)