Hasil untuk "Engineering machinery, tools, and implements"

Haolin Li, Michael Coblenz

Debugging is a central yet complex activity in software engineering. Prior studies have documented debugging strategies and tool usage, but little theory explains how experienced developers reason about bugs in large, real-world codebases. We conducted a qualitative study using a grounded theory approach. We observed seven professional developers and five professional live-coding streamers working on 17 debugging tasks in their own codebases, capturing diverse contexts of debugging. We theorize debugging as a structured, iterative diagnostic process in which programmers update a mental model of the system to guide information gathering. Developers gather information by alternating between navigation and execution strategies, employing forward and backward tracing modes of reasoning and adapting these approaches according to codebase context, complexity, and familiarity. Developers also gather external resources to complement code-based evidence, with their experience enabling them to systematically construct a mental model. We contribute a grounded theory of professional debugging that surfaces the human-centered dimensions of the practice, with implications for tool design and software engineering education.

Ömer Elri, Serkan Savaş

Manual notes and scattered messaging applications used in managing business processes compromise data integrity and abstract project tracking. In this study, an integrated system that works simultaneously on web and mobile platforms has been developed to enable individual users and teams to manage their workflows with concrete data. The system architecture integrates MongoDB, which stores data in JSON format, Node.js Express.js on the server side, React.js on the web interface, and React Native technologies on the mobile side. The system interface is designed around visual dashboards that track the status of tasks (To Do-In Progress-Done). The urgency of tasks is distinguished by color-coded labels, and dynamic graphics (Dashboard) have been created for managers to monitor team performance. The usability of the system was tested with a heterogeneous group of 10 people consisting of engineers, engineering students, public employees, branch managers, and healthcare personnel. In analyses conducted using a 5-point Likert scale, the organizational efficiency provided by the system compared to traditional methods was rated 4.90, while the visual dashboards achieved a perfect score of 5.00 with zero variance. Additionally, the ease of interface use was rated 4.65, and overall user satisfaction was calculated as 4.60. The findings show that the developed system simplifies complex work processes and provides a traceable digital working environment for Small and Medium-sized Enterprises and project teams.

Filipe Fernandes, Cláudia Werner

Context: Quantum Software Engineering (QSE) has emerged as a promising discipline to support the development of quantum applications by integrating quantum computing principles with established software engineering practices. Problem: Despite recent growth, QSE still lacks standardized methodologies, tools, and guidelines. Moreover, countries like Brazil have had minimal representation in the development of this emerging field. Objective: This study aims to map the current state of QSE by identifying research trends, contributions, and gaps that can inform future investigations and strategic initiatives. Methodology: A systematic mapping study was conducted across major scientific databases, retrieving 3,219 studies. After applying inclusion and exclusion criteria, 3,052 studies were excluded, resulting in 167 selected for analysis. The publications were classified by study type, research type, and alignment with SWEBOK knowledge areas. Results: Most studies focused on Software Engineering Models and Methods, Software Architecture, and Software Testing. Conceptual and technical proposals were predominant, while empirical validations remained limited. Conclusions: QSE is still a maturing field. Advancing it requires standardization, more empirical research, and greater participation from developing countries. As its main contribution, this study proposes a Brazilian Research Agenda in QSE to guide national efforts and foster the development of a strong local scientific community.

Agrawal Naman, Ridwan Shariffdeen, Guanlin Wang et al.

Large Language Models (LLMs) are becoming increasingly competent across various domains, educators are showing a growing interest in integrating these LLMs into the learning process. Especially in software engineering, LLMs have demonstrated qualitatively better capabilities in code summarization, code generation, and debugging. Despite various research on LLMs for software engineering tasks in practice, limited research captures the benefits of LLMs for pedagogical advancements and their impact on the student learning process. To this extent, we analyze 126 undergraduate students' interaction with an AI assistant during a 13-week semester to understand the benefits of AI for software engineering learning. We analyze the conversations, code generated, code utilized, and the human intervention levels to integrate the code into the code base. Our findings suggest that students prefer ChatGPT over CoPilot. Our analysis also finds that ChatGPT generates responses with lower computational complexity compared to CoPilot. Furthermore, conversational-based interaction helps improve the quality of the code generated compared to auto-generated code. Early adoption of LLMs in software engineering is crucial to remain competitive in the rapidly developing landscape. Hence, the next generation of software engineers must acquire the necessary skills to interact with AI to improve productivity.

Balaram Puli

This paper explores Site Reliability Engineering (SRE), a modern approach to maintaining scalable and reliable software systems. It presents observations on how structured SRE processes improve operational efficiency, reduce system downtime, and simplify maintenance. Drawing from real-world implementations, the study outlines key techniques in automation, monitoring, incident management, and deployment strategies. The work also highlights how these practices can be tailored to different environments, offering practical insights for engineers aiming to improve service reliability.

Sten Grüner, Nafise Eskandani

The integration of Industry 4.0 technologies into engineering workflows is an essential step toward automating and optimizing plant and process engineering processes. The Asset Administration Shell (AAS) serves as a key enabler for creating interoperable Digital Twins that facilitate engineering data exchange and automation. This paper explores the use of AAS within engineering workflows, particularly in combination with Business Process Model and Notation (BPMN) to define structured and automated processes. We propose a distributed AAS copy-on-write infrastructure that enhances security and scalability while enabling seamless cross organizational collaboration. We also introduce a workflow management prototype automating AAS operations and engineering workflows, improving efficiency and traceability.

António Galvão, Fernando Brito e Abreu, João Joanaz de Melo

Smart tourism (ST) stems from the concepts of e-tourism - focused on the digitalization of processes within the tourism industry, and digital tourism - also considering the digitalization within the tourist experience. The earlier ST references found regard ST Destinations and emerge from the development of Smart Cities. Our initial literature review on the ST concept and Smart Tourism Tools (STT) revealed significant research uncertainties: ST is poorly defined and frequently linked to the concept of Smart Cities; different authors have different, sometimes contradictory, views on the goals of ST; STT claims are often only based on technological aspects, and their "smartness" is difficult to evaluate; often the term "Smart" describes developments fueled by cutting-edge technologies, which lose that status after a few years. This chapter is part of the ongoing initiative to build an online observatory that provides a comprehensive view of STTs' offerings in Europe, known as the European STT Observatory. To achieve this, the observatory requires methodologies and tools to evaluate "smartness" based on a sound definition of ST and STT, while also being able to adapt to technological advancements. In this chapter, we present the results of a participatory approach where we invited ST experts from around the world to help us achieve this level of soundness. Our goal is to make a valuable contribution to the discussion on the definition of ST and STT.

Haoxiang Zhang, Shi Chang, Arthur Leung et al.

The rise of Foundation Models (FMs) like Large Language Models (LLMs) is revolutionizing software development. Despite the impressive prototypes, transforming FMware into production-ready products demands complex engineering across various domains. A critical but overlooked aspect is performance engineering, which aims at ensuring FMware meets performance goals such as throughput and latency to avoid user dissatisfaction and financial loss. Often, performance considerations are an afterthought, leading to costly optimization efforts post-deployment. FMware's high computational resource demands highlight the need for efficient hardware use. Continuous performance engineering is essential to prevent degradation. This paper highlights the significance of Software Performance Engineering (SPE) in FMware, identifying four key challenges: cognitive architecture design (i.e., the structural design that defines how AI components interact, reason, and interface with classical software components), communication protocols, tuning and optimization, and deployment. These challenges are based on literature surveys and experiences from developing an in-house FMware system. We discuss problems, current practices, and innovative paths for the software engineering community.

Cyril Picard, Faez Ahmed

In engineering design, navigating complex decision-making landscapes demands a thorough exploration of the design, performance, and constraint spaces, often impeded by resource-intensive simulations. Data-driven methods can mitigate this challenge by harnessing historical data to delineate feasible domains, accelerate optimization, or evaluate designs. However, the implementation of these methods usually demands machine-learning expertise and multiple trials to choose the right method and hyperparameters. This makes them less accessible for numerous engineering situations. Additionally, there is an inherent trade-off between training speed and accuracy, with faster methods sometimes compromising precision. In our paper, we demonstrate that a recently released general-purpose transformer-based classification model, TabPFN, is both fast and accurate. Notably, it requires no dataset-specific training to assess new tabular data. TabPFN is a Prior-Data Fitted Network, which undergoes a one-time offline training across a broad spectrum of synthetic datasets and performs in-context learning. We evaluated TabPFN's efficacy across eight engineering design classification problems, contrasting it with seven other algorithms, including a state-of-the-art AutoML method. For these classification challenges, TabPFN consistently outperforms in speed and accuracy. It is also the most data-efficient and provides the added advantage of being differentiable and giving uncertainty estimates. Our findings advocate for the potential of pre-trained models that learn from synthetic data and require no domain-specific tuning to make data-driven engineering design accessible to a broader community and open ways to efficient general-purpose models valid across applications. Furthermore, we share a benchmark problem set for evaluating new classification algorithms in engineering design.

Marvin Muñoz Barón, Marvin Wyrich, Daniel Graziotin et al.

Searching for clues, gathering evidence, and reviewing case files are all techniques used by criminal investigators to draw sound conclusions and avoid wrongful convictions. Similarly, in software engineering (SE) research, we can develop sound methodologies and mitigate threats to validity by basing study design decisions on evidence. Echoing a recent call for the empirical evaluation of design decisions in program comprehension experiments, we conducted a 2-phases study consisting of systematic literature searches, snowballing, and thematic synthesis. We found out (1) which validity threat categories are most often discussed in primary studies of code comprehension, and we collected evidence to build (2) the evidence profiles for the three most commonly reported threats to validity. We discovered that few mentions of validity threats in primary studies (31 of 409) included a reference to supporting evidence. For the three most commonly mentioned threats, namely the influence of programming experience, program length, and the selected comprehension measures, almost all cited studies (17 of 18) did not meet our criteria for evidence. We show that for many threats to validity that are currently assumed to be influential across all studies, their actual impact may depend on the design and context of each specific study. Researchers should discuss threats to validity within the context of their particular study and support their discussions with evidence. The present paper can be one resource for evidence, and we call for more meta-studies of this type to be conducted, which will then inform design decisions in primary studies. Further, although we have applied our methodology in the context of program comprehension, our approach can also be used in other SE research areas to enable evidence-based experiment design decisions and meaningful discussions of threats to validity.

Eriks Klotins, Michael Unterkalmsteiner, Panagiota Chatzipetrou et al.

Context: Software start-ups are emerging as suppliers of innovation and software-intensive products. However, traditional software engineering practices are not evaluated in the context, nor adopted to goals and challenges of start-ups. As a result, there is insufficient support for software engineering in the start-up context. Objective: We aim to collect data related to engineering goals, challenges, and practices in start-up companies to ascertain trends and patterns characterizing engineering work in start-ups. Such data allows researchers to understand better how goals and challenges are related to practices. This understanding can then inform future studies aimed at designing solutions addressing those goals and challenges. Besides, these trends and patterns can be useful for practitioners to make more informed decisions in their engineering practice. Method: We use a case survey method to gather first-hand, in-depth experiences from a large sample of software start-ups. We use open coding and cross-case analysis to describe and identify patterns, and corroborate the findings with statistical analysis. Results: We analyze 84 start-up cases and identify 16 goals, 9 challenges, and 16 engineering practices that are common among start-ups. We have mapped these goals, challenges, and practices to start-up life-cycle stages (inception, stabilization, growth, and maturity). Thus, creating the progression model guiding software engineering efforts in start-ups. Conclusions: We conclude that start-ups to a large extent face the same challenges and use the same practices as established companies. However, the primary software engineering challenge in start-ups is to evolve multiple process areas at once, with a little margin for serious errors.

Shih-Yuan Yu, Yonatan Gizachew Achamyeleh, Chonghan Wang et al.

Mission-critical embedded software is critical to our society's infrastructure but can be subject to new security vulnerabilities as technology advances. When security issues arise, Reverse Engineers (REs) use Software Reverse Engineering (SRE) tools to analyze vulnerable binaries. However, existing tools have limited support, and REs undergo a time-consuming, costly, and error-prone process that requires experience and expertise to understand the behaviors of software and vulnerabilities. To improve these tools, we propose $\textit{cfg2vec}$, a Hierarchical Graph Neural Network (GNN) based approach. To represent binary, we propose a novel Graph-of-Graph (GoG) representation, combining the information of control-flow and function-call graphs. Our $\textit{cfg2vec}$ learns how to represent each binary function compiled from various CPU architectures, utilizing hierarchical GNN and the siamese network-based supervised learning architecture. We evaluate $\textit{cfg2vec}$'s capability of predicting function names from stripped binaries. Our results show that $\textit{cfg2vec}$ outperforms the state-of-the-art by $24.54\%$ in predicting function names and can even achieve $51.84\%$ better given more training data. Additionally, $\textit{cfg2vec}$ consistently outperforms the state-of-the-art for all CPU architectures, while the baseline requires multiple training to achieve similar performance. More importantly, our results demonstrate that our $\textit{cfg2vec}$ could tackle binaries built from unseen CPU architectures, thus indicating that our approach can generalize the learned knowledge. Lastly, we demonstrate its practicability by implementing it as a Ghidra plugin used during resolving DARPA Assured MicroPatching (AMP) challenges.

Aritra Sarkar

This article provides a personal perspective on research in Automated Quantum Software Engineering (AQSE). It elucidates the motivation to research AQSE (why?), a precise description of such a framework (what?), and reflections on components that are required for implementing it (how?).

Yihao Sun, Jeffrey Ching, Kristopher Micinski

Binary reverse engineering is a challenging task because it often necessitates reasoning using both domain-specific knowledge (e.g., understanding entrypoint idioms common to an ABI) and logical inference (e.g., reconstructing interprocedural control flow). To help perform these tasks, reverse engineers often use toolkits (such as IDA Pro or Ghidra) that allow them to interactively explicate properties of binaries. We argue that deductive databases serve as a natural abstraction for interfacing between visualization-based binary analysis tools and high-performance logical inference engines that compute facts about binaries. In this paper, we present a vision for the future in which reverse engineers use a visualization-based tool to understand binaries while simultaneously querying a logical-inference engine to perform arbitrarily-complex deductive inference tasks. We call our vision declarative demand-driven reverse engineering (D^3RE for short), and sketch a formal semantics whose goal is to mediate interaction between a logical-inference engine (such Souffle) and a reverse engineering tool. We describe aprototype tool, d3re, which are using to explore the D^3RE vision. While still a prototype, we have used d3re to reimplement several common querying tasks on binaries. Our evaluation demonstrates that d3re enables both better performance and more succinct implementation of these common RE tasks.

Sabah Al-Fedaghi

This paper aims to contribute to further understanding of dynamism (the dynamic behavior of system models) in the mathematical and conceptual modeling of systems. This study is conducted in the context of the claim that software engineering lacks underpinning scientific theories, both for the software it produces and the processes by which it does so. The research literature proposes that information theory can provide such a benefit for software engineering. We explore the dynamism expressive power of conceptual modeling as a software engineering tool that can represent physical systems in the Shannon Weaver communication model (SWCM). Specifically, the modeled source in the SWCM is a physical phenomenon (a change that can occur in the world, e.g., tossing a coin) resulting in generating observable events and data of unaddressed information. The resultant model reflects the feasibility of extending the SWCM to be applied in conceptual modeling in software engineering.

T. F. M. Sirqueira, M. A. Miguel, H. L. O. Dalpra et al.

The experimental evaluation of the methods and concepts covered in software engineering has been increasingly valued. This value indicates the constant search for new forms of assessment and validation of the results obtained in Software Engineering research. Results are validated in studies through evaluations, which in turn become increasingly stringent. As an alternative to aid in the verification of the results, that is, whether they are positive or negative, we suggest the use of statistical methods. This article presents some of the main statistical techniques available, as well as their use in carrying out the implementation of data analysis in experimental studies in Software Engineering. This paper presents a practical approach proving statistical techniques through a decision tree, which was created in order to facilitate the understanding of the appropriate statistical method for each data analysis situation. Actual data from the software projects were employed to demonstrate the use of these statistical methods. Although it is not the aim of this work, basic experimentation and statistics concepts will be presented, as well as a concrete indication of the applicability of these techniques.

Michael Zargham, Zixuan Zhang, Victor Preciado

Decentralized Ledger Technology, popularized by the Bitcoin network, aims to keep track of a ledger of valid transactions between agents of a virtual economy without a central institution for coordination. In order to keep track of a faithful and accurate list of transactions, the ledger is broadcast and replicated across machines in a peer-to-peer network. To enforce validity of transactions in the ledger (i.e., no negative balance or double spending), the network as a whole coordinates to accept or reject new transactions based on a set of rules aiming to detect and block operations of malicious agents (i.e., Byzantine attacks). Consensus protocols are particularly important to coordinate operation of the network, since they are used to reconcile potentially conflicting versions of the ledger. Regardless of architecture and consensus mechanism used, resulting economic networks remain largely similar, with economic agents driven by incentives under a set of rules. Due to the intense activity in this area, proper mathematical frameworks to model and analyze behavior of blockchain-enabled systems are essential. In this paper, we address this need and provide the following contributions: (i) we establish a formal framework, with tools from dynamical systems theory, to mathematically describe core concepts in blockchain-enabled networks, (ii) we apply this framework to the Bitcoin network and recover its key properties, and (iii) we connect our modeling framework with powerful tools from control engineering, such as Lyapunov-like functions, to properly engineer economic systems with provable properties. Apart from the aforementioned contributions, the mathematical framework herein proposed lays a foundation for engineering more general economic systems built on emerging Turing complete networks, such as the Ethereum network, through which complex alternative economic models are explored.

Vahid Garousi, Ahmet Coşkunçay, Aysu Betin-Can et al.

Context: Understanding the types of software engineering practices and techniques used in the industry is important. There is a wide spectrum in terms of the types and maturity of software engineering practices conducted in each software team and company. To characterize the type of software engineering practices conducted in software firms, a variety of surveys have been conducted in different countries and regions. Turkey has a vibrant software industry and it is important to characterize and understand the state of software engineering practices in this industry. Objective: Our objective is to characterize and grasp a high-level view on type of software engineering practices in the Turkish software industry. Among the software engineering practices that we have surveyed in this study are the followings: software requirements, design, development, testing, maintenance, configuration management, release planning and support practices. The current survey is the most comprehensive of its type ever conducted in the context of Turkish software industry. Method: To achieve the above objective, we systematically designed an online survey with 46 questions based on our past experience in the Canadian and Turkish contexts and using the Software Engineering Body of Knowledge (SWEBOK). 202 practicing software engineers from the Turkish software industry participated in the survey. We analyze and report in this paper the results of the questions. Whenever possible, we also compare the trends and results of our survey with the results of a similar 2010 survey conducted in the Canadian software industry.

David Birch, Helen Liang, Paul H J Kelly et al.

This paper demonstrates a methodology to help practitioners maximise the utility of complex multidisciplinary engineering models implemented as spreadsheets, an area presenting unique challenges. As motivation we investigate the expanding use of Integrated Resource Management(IRM) models which assess the sustainability of urban masterplan designs. IRM models reflect the inherent complexity of multidisciplinary sustainability analysis by integrating models from many disciplines. This complexity makes their use time-consuming and reduces their adoption. We present a methodology and toolkit for analysing multidisciplinary engineering models implemented as spreadsheets to alleviate such problems and increase their adoption. For a given output a relevant slice of the model is extracted, visualised and analysed by computing model and interdisciplinary metrics. A sensitivity analysis of the extracted model supports engineers in their optimisation efforts. These methods expose, manage and reduce model complexity and risk whilst giving practitioners insight into multidisciplinary model composition. We report application of the methodology to several generations of an industrial IRM model and detail the insight generated, particularly considering model evolution.

Peter D. Turney, Michael Halasz

Diagnosing faults in aircraft gas turbine engines is a complex problem. It involves several tasks, including rapid and accurate interpretation of patterns in engine sensor data. We have investigated contextual normalization for the development of a software tool to help engine repair technicians with interpretation of sensor data. Contextual normalization is a new strategy for employing machine learning. It handles variation in data that is due to contextual factors, rather than the health of the engine. It does this by normalizing the data in a context-sensitive manner. This learning strategy was developed and tested using 242 observations of an aircraft gas turbine engine in a test cell, where each observation consists of roughly 12,000 numbers, gathered over a 12 second interval. There were eight classes of observations: seven deliberately implanted classes of faults and a healthy class. We compared two approaches to implementing our learning strategy: linear regression and instance-based learning. We have three main results. (1) For the given problem, instance-based learning works better than linear regression. (2) For this problem, contextual normalization works better than other common forms of normalization. (3) The algorithms described here can be the basis for a useful software tool for assisting technicians with the interpretation of sensor data.