Tool Builders Research Articles

Machine learning experiment management tools: a mixed-methods empirical study

Machine Learning (ML) experiment management tools support ML practitioners and software engineers when building intelligent software systems. By managing large numbers of ML experiments comprising many different ML assets, they not only facilitate engineering ML models and ML-enabled systems, but also managing their evolution—for instance, tracing system behavior to concrete experiments when the model performance drifts. However, while ML experiment management tools have become increasingly popular, little is known about their effectiveness in practice, as well as their actual benefits and challenges. We present a mixed-methods empirical study of experiment management tools and the support they provide to users. First, our survey of 81 ML practitioners sought to determine the benefits and challenges of ML experiment management and of the existing tool landscape. Second, a controlled experiment with 15 student developers investigated the effectiveness of ML experiment management tools. We learned that 70% of our survey respondents perform ML experiments using specialized tools, while out of those who do not use such tools, 52% are unaware of experiment management tools or of their benefits. The controlled experiment showed that experiment management tools offer valuable support to users to systematically track and retrieve ML assets. Using ML experiment management tools reduced error rates and increased completion rates. By presenting a user’s perspective on experiment management tools, and the first controlled experiment in this area, we hope that our results foster the adoption of these tools in practice, as well as they direct tool builders and researchers to improve the tool landscape overall.

Information process-system modelling for Circular Economy of manufacturing systems

Recent push towards sustainable development have lead machine tool builders to begin adopting novel business models which are usually peculiar in product-service-systems. In particular, business models as ‘pay-per-use’ introduced the issue of rental of production capacity integrated with the management of the life cycle of assets in a Circular Economy perspective. However, when it comes to machine tool builders, the high degree of customization of capital goods and in particular of multi-stage automated assembly systems that are strongly linked to the characteristics of the final product make this solution more difficult. Hence, the implementation of such business models represents a clear challenge for machine tool builders and manufacturing system suppliers from technological perspective. This work discusses preliminary results in the technological implications of such strategy. A real industrial case related to cross-sector multi-stage automated assembly systems is presented to highlight the technological challenges and potential research directions.

Identification of mobile development issues using semantic topic modeling of Stack Overflow posts

Background Increasing demands for mobile apps and services have recently led to an intensification of mobile development activities. With the proliferation of mobile development, there has been a major transformation in the architectures, paradigms, knowledge domains and skills of traditional software systems towards mobile development. Therefore, mobile developers experience a wide spectrum of issues specific to development processes of mobile apps and services. Methods In this article, we conducted a semantic content analysis based on topic modeling using mobile-related questions on Stack Overflow, a popular Q&A site for developers. With the aim of providing an understanding of the issues and challenges faced by mobile developers, we used a semi-automated methodology based on latent Dirichlet allocation (LDA), a probabilistic and generative approach for topic modeling. Results Our findings revealed that mobile developers’ questions focused on 36 topics in six main categories, including “Development”, “UI settings”, “Tools”, “Data Management”, “Multimedia”, and “Mobile APIs”. Besides, we investigated the temporal trends of the discovered issues and their relationships with mobile technologies. Our findings also revealed which issues are the most popular and which issues are the most difficult for mobile development. The methodology and findings of this study have valuable implications for mobile development stakeholders including tool builders, developers, researchers, and educators.

Machine Tool Builders in Basque Country Target Growth

Machine Tool Builders in Basque Country Target Growth

On the impact of single and co-occurrent refactorings on quality attributes in android applications

Android applications must evolve quickly to meet new user requirements, to facilitate bug fixing or to adapt to technological changes. This evolution can lead to various software quality problems that may hinder maintenance and further evolution. Code refactoring is a key practice that is employed to ensure that the intent of a code change is properly achieved without compromising internal software quality. While the impact of refactoring on software quality has been widely studied in object-oriented software in general, its impact in the specific context of mobile applications is still unclear. This paper reports on a large empirical study that aims to understand the impact of single and co-occurrent refactorings on quality metrics in Android applications. We analyze the evolution history of 800 open-source Android applications containing a total of 84,841 refactoring operations. We first analyze the impact of single refactoring operations on 21 common quality metrics using the Difference-in-Difference (DiD) statistical model. Then, we identify the most common co-occurrent refactorings using association rule mining, and investigate their impact on quality metrics using the DiD model. Our investigations deliver several important findings. Our results reveal that co-occurrent refactorings are quite prevalent in Android applications. Overall, 60% of the total number of refactoring commits contain multiple refactoring types, and 16 co-occurrent refactoring pairs tend to be applied together leading to a higher impact than single refactorings. We found that single refactorings have no statistically significant impact on quality metrics in 74.7% of the cases, a positive impact in 23.1% of the cases, and a negative impact in 2.2% of the cases. Whereas, co-occurrent refactorings have no statistically significant impact on quality metrics in 54.3% of the cases, a positive impact in 42.4% of the cases, and a negative impact in 3.3% of the cases. Our findings provide practical insights and suggest directions for researchers, practitioners, and tool builders to improve refactoring practices in the context of Android applications development.

Hybrid learning-based digital twin for manufacturing process: Modeling framework and implementation

Digital twin (DT) and artificial intelligence (AI) technologies are powerful enablers for Industry 4.0 toward sustainable resilient manufacturing. Digital twins of machine tools and machining processes combine advanced digital techniques and production domain knowledge, facilitate the enhancement of agility, traceability, and resilience of production systems, and help machine tool builders achieve a paradigm shift from one-time products provision to on-going service delivery. However, the adaptability and accuracy of digital twins at the shopfloor level are restricted by heterogeneous data sources, modeling precision as well as uncertainties from dynamical industrial environments. This article proposes a novel modeling framework to address these inadequacies by in-depth integrating AI techniques and machine tool expertise using aggregated data along the product development process. A data processing procedure is constructed to contextualize metadata sources from the design, planning, manufacturing, and quality stages and link them into a digital thread. On this consistent data basis, a modeling pipeline is presented to incorporate production and machine tool prior knowledge into AI development pipeline, while considering the multi-fidelity nature of data sources in dynamic industrial circumstances. In terms of implementation, we first introduce our existing work for building digital twins of machine tool and manufacturing process. Within this infrastructure, we developed a hybrid learning-based digital twin for manufacturing process following proposed modeling framework and tested it in an external industrial project exemplarily for real-time workpiece quality monitoring. The result indicates that the proposed hybrid learning-based digital twin enables learning uncertainties of the interaction of machine tools and machining processes in real industrial environments, thus allows estimating and enhancing the modeling reliability, depending on the data quality and accessibility. Prospectively, it also contributes to the reparametrization of model parameters and to the adaptive process control.

Large‐scale characterization of Java streams

AbstractJava streams are receiving the attention of developers targeting the Java virtual machine (JVM) as they ease the development of data‐processing logic, while also favoring code extensibility and maintainability through a concise and declarative style based on functional programming. Recent studies aim to shedding light on how Java developers use streams. However, they consider only small sets of applications and mainly apply manual code inspection and static analysis techniques. As a result, the large‐scale dynamic analysis of stream processing remains an open research question. In this article, we present the first large‐scale empirical study on the use of streams in Java code exercised via unit tests. We present stream‐analyzer, a novel dynamic program analysis (DPA) that collects runtime information and key metrics, which enable a fine‐grained characterization of sequential and parallel stream processing. We use a fully automatic approach to massively apply our DPA for the analysis of open‐source software projects hosted on GitHub. Our findings advance the understanding of the use of Java streams. Both the scale of our analysis and the profiling of dynamic information enable us to confirm with more confidence the outcome highlighted at a smaller scale by related work. Moreover, our study reports the popularity of many features of the Stream API and highlights multiple findings about runtime characteristics unique to streams, while also revealing inefficient stream processing and stream misuses. Finally, we present implications of our findings for developers of the Stream API, tool builders and researchers, and educators.

In the Age of Collaboration, the Computer-Aided Design Ecosystem is Behind: An Interview Study of Distributed CAD Practice

Computer-aided design (CAD) has become indispensable to increasingly collaborative hardware design processes. Despite the long-standing and growing need for collaboration with CAD models and tools, anecdotal reports and ongoing researcher efforts point to a complex and unresolved set of challenges faced by designers when working with distributed CAD. We aim to close this academic-practitioner knowledge gap through the first systematic study of professional user-driven CAD collaboration challenges. In this work, we conduct semi-structured interviews with 20 CAD professionals of diverse industries, roles, and experience levels to understand their collaborative workflows with distributed CAD tools. In total, we identify 14 challenges related to collaborative design, communication, data management, and permissioning that are currently impeding effective collaboration in professional CAD teams. Our systematic classification of CAD collaboration challenges presents a guide for pressing areas of future work, highlighting important implications for CAD researchers, practitioners, and tool builders to target new advancement in CAD infrastructure, management choices, and modelling best practices. With the insights gained from this work, we hope to ultimately improve collaboration efficiency, quality, and innovation for future product design teams.

Code Cloning in Smart Contracts on the Ethereum Platform: An Extended Replication Study

Smart contracts are programs deployed on blockchains that run upon meeting predetermined conditions. Once deployed, smart contracts are immutable, thus, defects in the deployed code cannot be fixed. As a consequence, software engineering anti-patterns, such as code cloning, pose a threat to code quality and security if unnoticed before deployment. In this paper, we report on the cloning practices of the Ethereum blockchain platform by analyzing 33,073 smart contracts amounting to over 4MLOC. Prior work reported an unusually high 79.2% of code clones in Ethereum smart contracts. We replicate this study at the conceptual level, i.e., we answer the same research questions by employing different methods. In particular, we analyze clones at the granularity of functions instead of code files, thereby providing a more fine-grained estimate of the clone ratio. Furthermore, we analyze more complex clone types, allowing for a richer analysis of cloning cases. To achieve this finer granularity of cloning analysis, we rely on the NiCad clone detection tool and extend it with support for Solidity, the programming language of the Ethereum platform. Our analysis shows that most findings of the original study hold at the finer granularity of our study as well; but also sheds light on some differences, and contributes new findings. Most notably, we report a 30.13% overall clone ratio, out of which 27.03% are exact duplicates. Our findings motivate improving the reuse mechanisms of Solidity, and in a broader context, of programming languages used for the development of smart contracts. Tool builders and language engineers can use this paper in the design and development of such reuse mechanisms. Business stakeholders can use this paper to better assess the security risks and technical outlooks of blockchain platforms.

Collaborative Model-Driven Software Engineering — A systematic survey of practices and needs in industry

The engineering of modern software-intensive systems is carried out in collaboration among stakeholders with specialized expertise. The complexity of such systems often also necessitates employing more rigorous approaches, such as Model-Driven Software Engineering (MDSE). Collaborative MDSE is the combination of the two disciplines, with its specific opportunities and challenges. The rapid expansion and maturation of the field started attracting tool builders from outside of academia. However, available systematic studies on collaborative MDSE focus exclusively on mapping academic research and fail to identify how academic research aligns with industry practices and needs. To address this shortcoming, we have carried out a mixed-method survey on the practices and needs concerning collaborative MDSE. First, we carried out a qualitative survey in two focus group sessions, interviewing seven industry experts. Second, based on the results of the interviews, we constructed a questionnaire and carried out a questionnaire survey with 41 industry expert participants. In this paper, we report the results of our study, investigate the alignment of academic research with the needs of practitioners, and suggest directions on research and development of the supporting techniques of collaborative MDSE.

Average Estimates in Line Graphs Are Biased Toward Areas of Higher Variability.

We investigate variability overweighting, a previously undocumented bias in line graphs, where estimates of average value are biased toward areas of higher variability in that line. We found this effect across two preregistered experiments with 140 and 420 participants. These experiments also show that the bias is reduced when using a dot encoding of the same series. We can model the bias with the average of the data series and the average of the points drawn along the line. This bias might arise because higher variability leads to stronger weighting in the average calculation, either due to the longer line segments (even though those segments contain the same number of data values) or line segments with higher variability being otherwise more visually salient. Understanding and predicting this bias is important for visualization design guidelines, recommendation systems, and tool builders, as the bias can adversely affect estimates of averages and trends.

THE INFLUENCE OF THE THERMAL REGIME ON THE GUIDEWAYS OF THE MAIN ACTUATION OF A GANTRY TYPE CNC MACHINE

CNC Gantry Type Machine Tools with great distance between slides (gantry milling machines, plasma and laser gantry cutting machines, vertical lathe machines, etc) are used extensively in the manufacturing of large and long components. Because this type of Machine Tool it is used to machine large parts at high speeds, significant heat will be produce at the cutting tool level and transmitted thru the tool holder to the ram or spindled and then to the ream case guideways. Heat it’s also generated when using a ball screw actuation for the main axis because of the friction forces at the ball screw level. Due to material expansion, the heat generated during the machining process can influence the precision of the Machine Tool. In this paper, finite element analysis (FEA) was used to analyze the influence of the temperature on the rigidity of the Machine Tool guideways. The results of the studies performed will provide the machine tool builder with important information regarding the points that need to be address in order to help in improving thermal compensation.

Little Tricky Logic: Misconceptions in the Understanding of LTL

Context: Linear Temporal Logic (LTL) has been used widely in verification. Its importance and popularity have only grown with the revival of temporal logic synthesis, and with new uses of LTL in robotics and planning activities. All these uses demand that the user have a clear understanding of what an LTL specification means. Inquiry: Despite the growing use of LTL, no studies have investigated the misconceptions users actually have in understanding LTL formulas. This paper addresses the gap with a first study of LTL misconceptions. Approach: We study researchers' and learners' understanding of LTL in four rounds (three written surveys, one talk-aloud) spread across a two-year timeframe. Concretely, we decompose "understanding LTL" into three questions. A person reading a spec needs to understand what it is saying, so we study the mapping from LTL to English. A person writing a spec needs to go in the other direction, so we study English to LTL. However, misconceptions could arise from two sources: a misunderstanding of LTL's syntax or of its underlying semantics. Therefore, we also study the relationship between formulas and specific traces. Knowledge: We find several misconceptions that have consequences for learners, tool builders, and designers of new property languages. These findings are already resulting in changes to the Alloy modeling language. We also find that the English to LTL direction was the most common source of errors; unfortunately, this is the critical "authoring" direction in which a subtle mistake can lead to a faulty system. We contribute study instruments that are useful for training learners (whether academic or industrial) who are getting acquainted with LTL, and we provide a code book to assist in the analysis of responses to similar-style questions. Grounding: Our findings are grounded in the responses to our survey rounds. Round 1 used Quizius to identify misconceptions among learners in a way that reduces the threat of expert blind spots. Rounds 2 and 3 confirm that both additional learners and researchers (who work in formal methods, robotics, and related fields) make similar errors. Round 4 adds deep support for our misconceptions via talk-aloud surveys. Importance This work provides useful answers to two critical but unexplored questions: in what ways is LTL tricky and what can be done about it? Our survey instruments can serve as a starting point for other studies.

Plant-pollinator Interaction Data: A case study of the WorldFAIR project

Biodiversity is a data-intensive science and relies on data from a large number of disciplines in order to build up a coherent picture of the extent and trajectory of life on earth (Bowker 2000). The ability to integrate such data from different disciplines, geographic regions and scales is crucial for making better decisions towards sustainable development. As the Biodiversity Information Standards (TDWG) community tackles standards development and adoption beyond its initial emphases on taxonomy and species distributions, expanding its impact and engaging a wider audience becomes increasingly important. Biological interactions data (e.g., predator-prey, host-parasite, plant-pollinator) have been a topic of interest within TDWG for many years and a Biological Interaction Data Interest Group (IG) was established in 2016 to address that issue. The IG has been working on the complexity of representing interactions data and surveying how Darwin Core (DwC, Wieczorek 2012) is being used to represent them (Salim 2022). The importance of cross-disciplinary science and data inspired the recently funded WorldFAIR project—Global cooperation on FAIR data policy and practice—coordinated by the Committee on Data of the International Science Council (CODATA), with the Research Data Alliance (RDA) as a major partner. WorldFAIR will work with a set of case studies to advance implementation of the FAIR data principles (Fig. 1). The FAIR data principles promote good practices in data management, by making data and metadata Findable, Accessible, Interoperable, and Reusable (Wilkinson 2016). Interoperability will be a particular focus to facilitate cross-disciplinary research. A set of recommendations and a framework for FAIR assessment in a set of disciplines will be developed (Molloy 2022). One of WorldFAIR's case studies is related to plant-pollinator interactions data. Its starting point is the model and schema proposed by Salim (2022) based on the DwC standard, which adheres to the diversifying GBIF data model strategy and on the Plant-Pollinator vocabulary described by Salim (2021). The case study on plant-pollinator interactions originated in the TDWG Biological Interaction Data Interest Group (IG) and within the RDA Improving Global Agricultural Data (IGAD) Community of Practice. IGAD is a forum for sharing experiences and providing visibility to research and work in food and agricultural data and has become a space for networking and blending ideas related to data management and interoperability. This topic was chosen because interoperability of plant-pollinator data is needed for better monitoring of pollination services, understanding the impacts of cultivated plants on wild pollinators and quantifying the contribution of wild pollinators to cultivated crops, understanding the impact of domesticated bees on wild ecosystems, and understanding the behaviour of these organisms and how this influences their effectiveness as pollinators. In addition to the ecological importance of these data, pollination is economically important for food production. In Brazil, the economic value of the pollination service was estimated at US$ 12 billion in 2018 (Wolowski 2019). All eleven case studies within the WorldFAIR project are working on FAIR Implementation Profiles (FIPs), which capture comprehensive sets of FAIR principle implementation choices made by communities of practice and which can accelerate convergence and facilitate cross-collaboration between disciplines (Schultes 2020). The FIPs are published through the FIP Wizard, which allows the creation of FAIR Enabling Resources. The FIPs creation will be repeated by the end of the project and capture results obtained from each case study in order to advance data interoperability. In the first FIP, resources from the Global Biodiversity Information Facility (GBIF) and Global Biotic Interactions (GloBI) were catalogued by the Plant-Pollinator Case Study team, and we expect to expand the existing FAIR Enabling Resources by the end of the project and contribute to plant-pollinator data interoperability and reuse. To tackle the challenge of promoting FAIR data for plant-pollinator interactions within the broad scope of the several disciplines and subdisciplines that generate and use them, we will conduct a survey of existing initiatives handling plant-pollinator interactions data and summarise the current status of best practices in the community. Once the survey is concluded, we will choose at least five agriculture-specific plant-pollination initiatives from our partners, to serve as targets for standards adoption. For data to be interoperable and reusable, it is essential that standards and best practices are community-developed to ensure adoption by the tool builders and data scientists across the globe. TDWG plays an important role in this scenario and we expect to engage the IG and other interested parties in that discussion.

How can machine tool builders capture value from smart services? Avoiding the service and digitalization paradox

PurposeThis paper aims to assess the existence of, or the risk of running into, a smart service paradox for industrial firms and how to overcome it.Design/methodology/approachA qualitative multiple case study is conducted involving four machine tool builders. The main source of data is formed by semi-structured interviews with service business managers. NVivo software was used to structure the interview harvest.FindingsThe findings reveal that a smart service paradox is a realistic threat for industrial firms, that smart service business development is a supply push affair rather than a matter of demand pull, that two types of permissions need to be granted by prospective users (license to operate and license to charge) and that three intermediate steps need to be undertaken and validated to overcome a smart service paradox: value testing or proofing; value recognition; and value sharing.Research limitations/implicationsThis study was vendor-centric and did not involve the industrial customers to whom the smart services were directed. It was based on a small sample, which limits the generalizability of findings to a broader or different (sectoral) context.Practical implicationsLessons are identified for service managers on how to circumvent a smart service paradox.Originality/valueThis study departs from a value creation-delivery-capture (“business model”) perspective to assess smart service paradox dynamics. By adopting a relational perspective to it, the present paper succeeds in presenting a more granular version of the base business model.

Bugs in Quantum computing platforms: an empirical study

The interest in quantum computing is growing, and with it, the importance of software platforms to develop quantum programs. Ensuring the correctness of such platforms is important, and it requires a thorough understanding of the bugs they typically suffer from. To address this need, this paper presents the first in-depth study of bugs in quantum computing platforms. We gather and inspect a set of 223 real-world bugs from 18 open-source quantum computing platforms. Our study shows that a significant fraction of these bugs (39.9%) are quantum-specific, calling for dedicated approaches to prevent and find them. The bugs are spread across various components, but quantum-specific bugs occur particularly often in components that represent, compile, and optimize quantum programming abstractions. Many quantum-specific bugs manifest through unexpected outputs, rather than more obvious signs of misbehavior, such as crashes. Finally, we present a hierarchy of recurrent bug patterns, including ten novel, quantum-specific patterns. Our findings not only show the importance and prevalence bugs in quantum computing platforms, but they help developers to avoid common mistakes and tool builders to tackle the challenge of preventing, finding, and fixing these bugs.

Implementation of Digital Twin-based Virtual Commissioning in Machine Tool Manufacturing

Virtual commissioning is not a new concept; However, it is all the rage with the introduction of Industry 4.0, in the field of product lifecycle management, computer-aided design (CAD), computer-aided manufacturing (CAM), and within the industrial automation programming frameworks. Although, this is a very active area of research and innovation, these technologies have little implementation in the machine tool industry [11]. There is still no integrated simulation environment for virtual commissioning in the market. In this context, digitalisation is a key driver. The aim of this paper is to describe the practice of virtual commissioning in the machine tool manufacturing industry by identifying available solutions in the market and addressing the challenges faced within the machine tool sector. As a result, a digital twin based virtual commissioning solution has been developed at Danobatgroup, the leading machine tool builder in Spain, which is a step forward towards the digitalisation of machine tool manufacturing.

On the (un‐)adoption of JavaScript front‐end frameworks

AbstractJavaScript is characterized by a rich ecosystem of libraries and frameworks. A key element in this ecosystem are frameworks used for implementing the front‐end of web‐based applications, such as Vue and React. However, despite their relevance, we have few works investigating the factors that drive the adoption—and un‐adoption—of front‐end‐based JavaScript frameworks. Therefore, in this article, we first report the results of a survey with 49 developers where we asked them to describe the factors they consider when selecting a front‐end framework. In the second part of the work, we focus on projects that migrate from one framework to another since JavaScript's ecosystem is also very dynamic. Finally, we provide a quantitative characterization of the migration effort and reveal the main barriers faced by the developers during this effort. Although not completely generalizable, our central findings are as follows: (a) popularity and learnability are the key factors that motivate the choice of front‐end frameworks in JavaScript; (b) from the 49 surveyed developers, one out of four have plans to migrate to another framework in the future; (c) the time spent performing the migration is greater than or equal to the time spent using the old framework in all studied projects. We conclude with a list of implications for practitioners, framework developers, tool builders, and researchers.

Influence of the positioning accuracy given by the positioning encoder of the CNC machine tools

The CNC machine tools are present at a large extent worldwide, for machining by cutting all kinds of work pieces. Their machining accuracy and quality are crucial to obtaining very accurate and intricate work pieces. The accuracy of a CNC machine tool is given by the quality of each and every kinematic linkage/coupling in its structure. This work aims at researching the influence of several constructive and functional factors on the positioning accuracy of a kinematic coupling in the structure of a CNC machine tool. The influence caused by noncompliance with the Abbé principle on the positioning accuracy of a feed kinematical linkage is analysed into detail, both at idle and load running. Out of this research useful conclusions are resulting for the CNC machine tool designers and builders, with respect to optimising several constructive factors, such as the location of the position encoder, location of the ball screw, stiffness of the directional guideways etc., in order to improve the accuracy of the CNC machine tool. The results of the theoretical research have been validated through experimental trials, confirming the impossibility of full compliance with the Abbé on the CNC machine tools.

The effects of the bending moment upon the mobile element of CNC Machine Tools with great distance between slides

CNC Machine Tools with kinematical feed chains with great distance between slides (gantry milling machines, cutting machines, carousel lathes etc.) are utilized extensively for machining large and heavy parts. In this type of machine, we can observe a specific phenomenon where the gravity center of the vertical actuated elements moves in the direction of the balancing force for the mobile elements, causing a bending moment to be induced in the mobile element (the mechanical coupling between the two kinematical feed chains). This effect leads to deformations and in worse case looking of the mobile element during movement, all of this has a detrimental effect on machining accuracy and efficiency. In this paper, using finite element analysis (FEA) several studies where preformed. A static analysis that calculates the stresses, displacements and strains. A modal analysis to determine the first six natural frequencies and modal deformed shapes. A harmonic response analysis to determine the response of the mobile element structure. This studies will highlight the week points of mobile element and will provide the machine tool builder with important information regarding the points that need to be address in order to improve the behavior of the mobile element during actuation.