Search

The Doyle-Fuller-Newman model is arguably the most ubiquitous electrochemical model in lithium-ion battery research. Since it is a highly nonlinear model, its input-output relations are still poorly understood. Researchers therefore often employ sensitivity analyses to elucidate relative parametric importance for certain use cases. However, some methods are ill-suited for the complexity of the model and appropriate methods often face the downside of only being applicable to scalar quantities of interest. We implement a novel framework for global sensitivity analysis of time-dependent model outputs and apply it to a drive cycle simulation. We conduct a full and a subgroup sensitivity analysis to resolve lowly sensitive parameters and explore the model error when unimportant parameters are set to arbitrary values. Our findings suggest that the method identifies insensitive parameters whose variations cause only small deviations in the voltage response of the model. By providing the methodology, we hope research questions related to parametric sensitivity for time-dependent quantities of interest, such as voltage responses, can be addressed more easily and adequately in simulative battery research and beyond.

[Context] Technical debt (TD) in machine learning (ML) systems, much like its counterpart in software engineering (SE), holds the potential to lead to future rework, posing risks to productivity, quality, and team morale. Despite growing attention to TD in SE, the understanding of ML-specific code-related TD remains underexplored. [Objective] This paper aims to identify and discuss the relevance of code-related issues that lead to TD in ML code throughout the ML workflow. [Method] The study first compiled a list of 34 potential issues contributing to TD in ML code by examining the phases of the ML workflow, their typical associated activities, and problem types. This list was refined through two focus group sessions involving nine experienced ML professionals, where each issue was assessed based on its occurrence contributing to TD in ML code and its relevance. [Results] The list of issues contributing to TD in the source code of ML systems was refined from 34 to 30, with 24 of these issues considered highly relevant. The data pre-processing phase was the most critical, with 14 issues considered highly relevant. Shortcuts in code related to typical pre-processing tasks (e.g., handling missing values, outliers, inconsistencies, scaling, rebalancing, and feature selection) often result in "patch fixes" rather than sustainable solutions, leading to the accumulation of TD and increasing maintenance costs. Relevant issues were also found in the data collection, model creation and training, and model evaluation phases. [Conclusion] We have made the final list of issues available to the community and believe it will help raise awareness about issues that need to be addressed throughout the ML workflow to reduce TD and improve the maintainability of ML code., Comment: To appear in the 2025 4th IEEE/ACM International Conference on AI Engineering - Software Engineering for AI, CAIN 2025

[Context and Motivation] Several studies have investigated attributes of great software practitioners. However, the investigation of such attributes is still missing in Requirements Engineering (RE). The current knowledge on attributes of great software practitioners might not be easily translated to the context of RE because its activities are, usually, less technical and more human-centered than other software engineering activities. [Question/Problem] This work aims to investigate which are the attributes of great requirements engineers, the relationship between them, and strategies that can be employed to obtain these attributes. We follow a method composed of a survey with 18 practitioners and follow up interviews with 11 of them. [Principal Ideas/Results] Investigative ability in talking to stakeholders, judicious, and understand the business are the most commonly mentioned attributes amongst the set of 22 attributes identified, which were grouped into four categories. We also found 38 strategies to improve RE skills. Examples are training, talking to all stakeholders, and acquiring domain knowledge. [Contribution] The attributes, their categories, and relationships are organized into a map. The relations between attributes and strategies are represented in a Sankey diagram. Software practitioners can use our findings to improve their understanding about the role and responsibilities of requirements engineers.

Context: This study explores how software professionals identify and address biases in AI systems within the software industry, focusing on practical knowledge and real-world applications. Goal: We aimed to understand the strategies employed by practitioners to manage bias and their implications for fairness debt. Method: We used a qualitative research method, gathering insights from industry professionals through interviews and employing thematic analysis to explore the collected data. Findings: Professionals identify biases through discrepancies in model outputs, demographic inconsistencies, and issues with training data. They address these biases using strategies such as enhanced data management, model adjustments, crisis management, improving team diversity, and ethical analysis. Conclusion: Our paper presents initial evidence on addressing fairness debt and provides a foundation for developing structured guidelines to manage fairness-related issues in AI systems.

Background: The use of gray literature (GL) has grown in software engineering research, especially in studies that consider Questions and Answers (Q&A) sites, since software development professionals widely use them. Though snowballing (SB) techniques are standard in systematic literature reviews, little is known about how to apply them to gray literature reviews. Aims: This paper investigates how to use SB approaches on Q&A sites during gray literature reviews to identify new valid discussions for analysis. Method: In previous studies, we compiled and analyzed a set of Stack Exchange Project Management (SEPM) discussions related to software engineering technical debt (TD). Those studies used a data set consisting of 108 valid discussions extracted from SEPM. Based on this start data set, we perform forward and backward SB using two different approaches: link-based and similarity-based SB. We then compare the precision and recall of those two SB approaches against the search-based approach of the original study. Results: In just one snowballing iteration, the approaches yielded 291 new discussions for analysis, 130 of which were considered valid for our study. That is an increase of about 120% over the original data set (recall). The SB process also yielded a similar rate of valid discussion retrieval when compared to the search-based approach (precision). Conclusion: This paper provides guidelines on how to apply two SB approaches to find new valid discussions for review. To our knowledge, this is the first study that analyzes the use of SB on Q&A websites. By applying SB, it was possible to identify new discussions, significantly increasing the relevant data set for a gray literature review., Comment: 10 pages, 6 figures

Central Bank Digital Currency (CBDC) can be defined as a virtual currency based on node network and digital encryption algorithm issued by a country which has a legal credit protection. CBDCs are supported by Distributed Ledger Technologies (DLTs), and they may allow a universal means of payments for the digital era. There are many ways to proceed, they all require central banks to develop technological expertise. Considering these points, it is important to understand the new IT governance in the financial markets due to CBDC and digital economy. Information Technology is an essential driver that will allow the new financial industry design. This paper has the objective to answer two questions through an updated Systematic Literature Review (SLR). The first question is What IT resources and tools have been considered or applied to set the governance of CBDC adoption? The second; Identify IT governance models in the financial market due to CBDC adoption. Bank for International Settlements (BIS) publications, Scopus and Web of Science were considered as sources of studies. After the strings and including criteria were applied, fourteen papers were analyzed. This paper finds many IT resources used in the CBDC adoption and some preliminary IT design related to the IT governance of CBDC, in the results and discussion section the findings are more detailed. Finally, limitations and future work are considered. Keywords: Blockchain, Central Bank Digital Currency (CBDC), Digital Economy, Distributed Ledger Technology (DLT), Information Technology (IT), IT governance., Comment: Keywords: Blockchain, Central Bank Digital Currency (CBDC), Digital Economy, Distributed Ledger Technology (DLT), Information Technology (IT), IT governance

As software systems continue to play a significant role in modern society, ensuring their fairness has become a critical concern in software engineering. Motivated by this scenario, this paper focused on exploring the multifaceted nature of bias in software systems, aiming to provide a comprehensive understanding of its origins, manifestations, and impacts. Through a scoping study, we identified the primary causes of fairness deficiency in software development and highlighted their adverse effects on individuals and communities, including instances of discrimination and the perpetuation of inequalities. Our investigation culminated in the introduction of the concept of software fairness debt, which complements the notions of technical and social debt, encapsulating the accumulation of biases in software engineering practices while emphasizing the societal ramifications of bias embedded within software systems. Our study contributes to a deeper understanding of fairness in software engineering and paves the way for the development of more equitable and socially responsible software systems.

Grain boundary (GB) properties greatly influence the mechanical, electrical, and thermal response of polycrystalline materials. Most computational studies of GB properties at finite temperatures use molecular dynamics (MD), which is computationally expensive, limited in the range of accessible timescales, and requires cumbersome techniques like thermodynamic integration to estimate free energies. This restricts the reasonable computation (without incurring excessive computational expense) of GB properties to regimes that are often unrealistic, such as zero temperature or extremely high strain rates. Consequently, there is a need for simulation methodology that avoids the timescale limitations of MD, while providing reliable estimates of GB properties. The Gaussian Phase-Packet (GPP) method is a temporal coarse-graining technique that can predict relaxed atomic structures at finite temperature in the quasistatic limit. This work applies GPP, combined with the quasiharmonic approximation for computing the free energy, to the problem of determining the free energy and shear coupling factor of grain boundaries in metals over a range of realistic temperatures. Validation is achieved by comparison to thermodynamic integration, which confirms that the presented approach captures relaxed-energy GB structures and shear coupling factors at finite temperature with a high degree of accuracy.

In real-world scenarios we often need to perform multiple tasks simultaneously. Multi-Task Learning (MTL) is an adequate method to do so, but usually requires datasets labeled for all tasks. We propose a method that can leverage datasets labeled for only some of the tasks in the MTL framework. Our work, Knowledge Assembly (KA), learns multiple tasks from disjoint datasets by leveraging the unlabeled data in a semi-supervised manner, using model augmentation for pseudo-supervision. Whilst KA can be implemented on any existing MTL networks, we test our method on jointly learning person re-identification (reID) and pedestrian attribute recognition (PAR). We surpass the single task fully-supervised performance by $4.2\%$ points for reID and $0.9\%$ points for PAR., Comment: Accepted at CVPRW'23

Overcoming the time scale limitations of atomistics can be achieved by switching from the state-space representation of Molecular Dynamics (MD) to a statistical-mechanics-based representation in phase space, where approximations such as maximum-entropy or Gaussian phase packets (GPP) evolve the atomistic ensemble in a time-coarsened fashion. In practice, this requires the computation of expensive high-dimensional integrals over all of phase space of an atomistic ensemble. This, in turn, is commonly accomplished efficiently by low-order numerical quadrature. We show that numerical quadrature in this context, unfortunately, comes with a set of inherent problems, which corrupt the accuracy of simulations -- especially when dealing with crystal lattices with imperfections. As a remedy, we demonstrate that Graph Neural Networks, trained on Monte-Carlo data, can serve as a replacement for commonly used numerical quadrature rules, overcoming their deficiencies and significantly improving the accuracy. This is showcased by three benchmarks: the thermal expansion of copper, the martensitic phase transition of iron, and the energy of grain boundaries. We illustrate the benefits of the proposed technique over classically used third- and fifth-order Gaussian quadrature, we highlight the impact on time-coarsened atomistic predictions, and we discuss the computational efficiency. The latter is of general importance when performing frequent evaluation of phase space or other high-dimensional integrals, which is why the proposed framework promises applications beyond the scope of atomistics.

The observation of energetic astronomical sources emitting very high-energy gamma-rays in the TeV spectral range (as e.g. supernova remnants or blazars) is mainly based on detecting the Cherenkov light induced by relativistic particles in the showers produced by the photon interaction with the Earth atmosphere. The ASTRI Mini-Array is an INAF-led project aimed observing such celestial objects in the 1 - 100 TeV energy range. It consists of an array of nine innovative imaging atmospheric Cherenkov telescopes that are an evolution of the dual-mirror aplanatic ASTRI-Horn telescope operating at the INAF "M.C. Fracastoro" observing station (Serra La Nave, Mount Etna, Italy). The ASTRI Mini-Array is currently under construction at the Observatorio del Teide (Tenerife, Spain). In this paper, we present the compact (diameter 660mm, height 520mm, weight 73kg) ASTRI-Horn prototype Cherenkov Camera based on a modular multipixel Silicon Photon Multiplier (SiPM) detector, has been acquiring data since 2016 and allowing us to obtain both scientific data and essential lessons. In this contribution, we report the main features of the camera and its evolution toward the new Cherenkov camera, which will be installed on each ASTRI Mini-Array telescope to cover an unprecedented field of view of 10.5{\deg}., Comment: 10 pages, 7 figures, 2022 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector (RTSD) Conference in press by IEEE Xplore

Context: Advances in technical debt research demonstrate the benefits of applying the financial debt metaphor to support decision-making in software development activities. Although decision-making during requirements engineering has significant consequences, the debt metaphor in requirements engineering is inadequately explored. Objective: We aim to conceptualize how the debt metaphor applies to requirements engineering by organizing concepts related to practitioners' understanding and managing of requirements engineering debt (RED). Method: We conducted two in-depth expert interviews to identify key requirements engineering debt concepts and construct a survey instrument. We surveyed 69 practitioners worldwide regarding their perception of the concepts and developed an initial analytical theory. Results: We propose a RED theory that aligns key concepts from technical debt research but emphasizes the specific nature of requirements engineering. In particular, the theory consists of 23 falsifiable propositions derived from the literature, the interviews, and survey results. Conclusions: The concepts of requirements engineering debt are perceived to be similar to their technical debt counterpart. Nevertheless, measuring and tracking requirements engineering debt are immature in practice. Our proposed theory serves as the first guide toward further research in this area., Comment: Revised manuscript based on reviews

Surface energies and surface elasticity largely affect the mechanical response of nanostructures as well as the physical phenomenon associated with surfaces such as evaporation and adsorption. Studying surface energies at finite temperatures is therefore of immense interest for nanoscale applications. However, calculating surface energies and derived quantities from atomistic ensembles is usually limited to zero temperature or involve cumbersome thermodynamic integration techniques at finite temperature. Here, we illustrate a technique to identify the energy and elastic properties of surfaces of solids at non-zero temperature based on a Gaussian phase packets (GPP) approach (which in the isothermal limit coincides with a maximum-entropy formulation). Using this setup, we investigate the effect of temperature on the surface properties of different crystal faces for six pure metals -- copper, nickel, alumimum, iron, tungsten and vanadium -- thus covering both FCC and BCC lattice structures. While the obtained surface energies and stresses usually show a decreasing trend with increasing temperature, the elastic constants do not show such a consistent trend across the different materials and are quite sensitive to temperature changes. Validation is performed by comparing the obtained surface energy densities of selected BCC and FCC materials to those calculated via molecular dynamics.

The technical debt (TD) metaphor describes actions made during various stages of software development that lead to a more costly future regarding system maintenance and evolution. According to recent studies, on average 25% of development effort is spent, i.e. wasted, on TD caused issues in software development organizations. However, further research is needed to investigate the relations between various software development activities and TD. The objective of this study is twofold. First, to get empirical insight on the understanding and the use of the TD concept in the IT industry. Second, to contribute towards precise conceptualization of the TD concept through analysis of causes and effects. In order to address the research objective a family of surveys was designed as a part of an international initiative that congregates researchers from 12 countries -- InsighTD. At country level, national teams ran survey replications with industry practitioners from the respective countries. In total 653 valid responses were collected from 6 countries. Regarding the prevalence of the TD concept 22% of practitioners have only theoretical knowledge about it, and 47% have some practical experiences with TD identification or management. Further analysis indicated that senior practitioners who work in larger organizations, larger teams, and on larger systems are more likely to be experienced with TD management. Time pressure or deadline was the single most cited cause of TD. Regarding the effects of TD: delivery delay, low maintainability, and rework were the most cited. InsighTD is the first family of surveys on technical debt in software engineering. It provided a methodological framework that allowed multiple replication teams to conduct research activities and to contribute to a single dataset. Future work will focus on more specific aspects of TD management., Comment: Journal of Systems and Software

Context: Contemporary software development is typically conducted in dynamic, resource-scarce environments that are prone to the accumulation of technical debt. While this general phenomenon is acknowledged, what remains unknown is how technical debt specifically manifests in and affects software processes, and how the software development techniques employed accommodate or mitigate the presence of this debt. Objectives: We sought to draw on practitioner insights and experiences in order to classify the effects of agile method use on technical debt management. We explore the breadth of practitioners' knowledge about technical debt; how technical debt is manifested across the software process; and the perceived effects of common agile software development practices and processes on technical debt. In doing so, we address a research gap in technical debt knowledge and provide novel and actionable managerial recommendations. Method: We designed, tested and executed a multi-national survey questionnaire to address our objectives, receiving 184 responses from practitioners in Brazil, Finland, and New Zealand. Results: Our findings indicate that: 1) Practitioners are aware of technical debt, although, there was under utilization of the concept, 2) Technical debt commonly resides in legacy systems, however, concrete instances of technical debt are hard to conceptualize which makes it problematic to manage, 3) Queried agile practices and processes help to reduce technical debt; particularly, techniques that verify and maintain the structure and clarity of implemented artifacts. Conclusions: The fact that technical debt instances tend to have characteristics in common means that a systematic approach to its management is feasible. However, notwithstanding the positive effects of some agile practices on technical debt management, competing stakeholders' interests remain a concern.(Abridged), Comment: Journal paper, 23 pages, 7 figures, 2 tables