Software Engineering Applications Research Articles

Development of Arduino applications for IoT applications in software engineering education: a systematic literature review

The continuous development of software applications is a necessary step in producing high-quality products that will consistently meet end-user expectations and stakeholder needs. Development of Arduino applications, embedded in a product’s hardware, can and should be considered at the software engineering phase itself, even though current practice dictates it be handled by product engineers. The method used in this investigation was based on a systematic literature review (SLR). Therefore, this paper depicts a gap that currently exists within the body of literature surrounding the development of Arduino applications for ‘internet of things’ (IoT) applications in software engineering education in commercial and research fields. The result of this study are two findings investigates: i) relevant Arduino application development used in software engineering and ii) method for applying software engineering for Arduino applications. The limitations and constraints of each technique in respect to Arduino apps were also examined in order to provide a better understanding of each body of study's weaknesses and strengths. We realise that these studies are still insufficient and need to be evaluated and improved further.

Development of carbon dioxide sensor using fiber optic-based refractometer

In this work, a fiber optic-based refractometer has continuously been developed for measuring carbon dioxide (CO2) using the evanescent wave method. Consequently, this technique is utilized to determine the light absorption values. Further, the chemical compound is also used to coat onto the modified sensing probe. However, this probe is, generally, made from multi-mode fiber (MMF). Moreover, the principle of altering the refractive index (RI) value is applied by removing the cladding of the sensing probe using hydrofluoric acid (HF). In addition, the zinc oxide (ZnO) mixed with the sodium silicate (Na2SiO3), known for its CO2 absorption capabilities, is used as a compound to coat the modified cladding. Besides, the sensing probe has been developed in three selected lengths (L) which are 10, 20, and 25 mm, and then tested from 0 to 40 % of CO2 concentration. Moreover, a biogas analyzer has been operated as a standard reference instrument. Afterward, an engineering application software is also developed for data processing to analyze the data in the relationship between the differential voltage, percentage of attenuation, and percentage of concentration versus time domain, respectively. The experimental results reported that the developed sensing can detect CO2 in any sensing length from 8.11 to 38.25 % with a sensitivity of 0.0102 mV/%concentration. In addition, a percentage attenuation of 2.11 %, 14.52 %, and 29.29 % is achieved in whole sensing lengths. However, this analyzes that at 10 mm the probe is more suitable due to its high responsibility to detect the CO2. Furthermore, the experimental results showed that the selected compound can recover with a recovery time of approximately 6 min.

On the Practical Power of Automata in Pattern Matching

Many papers in the intersection of theoretical and applied algorithms show that the simple, asymptotically less efficient algorithm, performs better than the bestcomplex theoretical algorithms on random data or in specialized “real world” applications. This paper considers the Knuth–Morris–Pratt automaton, and shows a counter-intuitive practical result. The classical pattern matching paradigm is that of seeking occurrences of one string—the pattern, in another—the text, where both strings are drawn from an alphabet set Σ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Sigma$$\\end{document}. Assuming the text length is n and the pattern length is m, this problem can naively be solved in time O(nm). In Knuth, Morris and Pratt’s seminal paper of 1977, an automaton, was developed that allows solving this problem in time O(n) for any alphabet. This automaton, which we will refer to as the KMP-automaton, has proven useful in solving many other problems. A notable example is the parameterized pattern matching model. In this model, a consistent renaming of symbols from Σ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Sigma$$\\end{document} is allowed in a match. The parameterized matching paradigm has proven useful in problems in software engineering, computer vision, and other applications. It has long been believed that for texts where the symbols are uniformly random, the naive algorithm will perform as well as the KMP algorithm. In this paper, we examine the practical efficiency of the KMP algorithm versus the naive algorithm on a randomly generated text. We analyze the time under various parameters, such as alphabet size, pattern length, and the distribution of pattern occurrences in the text. We do this for both the original exact matching problem and parameterized matching. While the folklore wisdom is vindicated by these findings for the exact matching case, surprisingly, the KMP algorithm always works significantly faster than the naive in the parameterized matching case. We check this hypothesis for DNA texts and image data and observe a similar behavior as in the random text. We also show a very structured exact matching case where the automaton is much more efficient.

Research artifacts in software engineering publications: Status and trends

The Software Engineering (SE) community has been embracing the open science policy and encouraging researchers to disclose artifacts in their publications. However, the status and trends of artifact practice and quality remain unclear, lacking insights on further improvement. In this paper, we present an empirical study to characterize the research artifacts in SE publications. Specifically, we manually collect 1,487 artifacts from all 2,196 papers published in top-tier SE conferences (ASE, FSE, ICSE, and ISSTA) from 2017 to 2022. We investigate the common practices (e.g., URL location and format, storage websites), maintenance activities (e.g., last update time and URL validity), popularity (e.g., the number of stars on GitHub and characteristics), and quality (e.g., documentation and code smell) of these artifacts. Based on our analysis, we reveal a rise in publications providing artifacts. The usage of Zenodo for sharing artifacts has significantly increased. However, artifacts stored in GitHub tend to receive few stars, indicating a limited influence on real-world SE applications. We summarize the results and provide suggestions to different stakeholders in conjunction with current guidelines.

Applying Software Engineering Based on Peer to Peer ‎‎Communication

The JavaScript programming language was selected to create software creation that facilitates the creation of a video connection between users because it enables the creation of cross-platform apps relatively quickly. For example, “Web Real-Time Communication (WebRTC)” standards do not specify exactly how two browsers initiate and manage communication with one another. The reason for this is that WebRTC does not specify the signaling technique or protocol. This paper's main goal is to design and construct a WebRTC simultaneous video conference between peers utilizing Google Chrome and the Socket.io signaling technology. A Local Area Network was used in this experiment (LAN). Furthermore, an assessment was conducted on the quality of experience (QoE), the Socket.io signaling method, and resources, including bandwidth consumption. This paper describes the simultaneous execution of peer-to-peer video calls with user identification (user-id).

АНАЛИЗ НОВЫХ ТЕХНОЛОГИЙ РАЗРАБОТКИ ПРОГРАММНОГО ОБЕСПЕЧЕНИЯ В ИНТЕРНЕТЕ

Due to the rapid development of big data and Internet technologies, the field of application of software development has gradually expanded, which has played an important role in promoting the development of many areas and has allowed us to achieve good results in the relevant areas. With the development of the Internet+ in modern society, users have a variety of requirements for software functions. They need to actively explore new technologies and continuously develop software, thereby contributing to the sustainable development of software development and benefiting the development of human society. Thus, this article mainly examines the need to develop new technologies to promote the development of software engineering, analyzes the areas of application of software engineering and existing problems in the current development of software engineering, and then analyzes and proposes the future of new technologies for software development in the era of “Internet+”.

FFEA software: Repeated early innovation

FFEA software: Repeated early innovation Dr Joanna Leng, Senior Research Software Engineering Fellow at the University of Leeds, discusses software with repeated early innovation using the example of the FFEA software. This article analyses the application of software engineering (SE) tools and practices and research software engineering (RSE) practices to the Fluctuating Finite Element Analysis (FFEA) software, a mesoscale modelling package. In 2016, I met the FFEA team to discuss improving their software. In 2020, I started working with them to develop a roadmap for the FFEA software by assessing their research interests and software.

Multi-scale context feature and cross-attention network-enabled system and software-based for pavement crack detection

Pavement crack detection continues to be a stubborn problem given the interference of various factors in the actual pavement and the complex topological structure of asphalt pavement. Among all the obstacles, the bottleneck of pavement crack detection lies in the difficulty of segmenting the cracks in the pavement images whose edges are blurred. This paper proposes a multi-scale context feature and cross-attention based on convolutional neural network for accurate and robust pavement crack segmentation. The multi-scale context feature module is built in different deep networks to extract rich crack feature information. Subsequently, in order to effectively promote the seamless integration of features at different levels, we deploy cross-attention modules to each branch. After that, we add deep supervision to each branch to accelerate training. Finally, we integrate the outputs of each branch to obtain the final output diagram. The comparative experiments on various pavement datasets show that the method has better robustness. At the same time, this paper designs a complete system of pavement crack detection (PCD) and develops corresponding engineering application software. The PCD system can record the real-time pavement image data to the edge server, and the client can also monitor the real-time pavement images from the edge server through the HTTP protocol.

QuoTe : Quality-oriented Testing for Deep Learning Systems

Recently, there has been significant growth of interest in applying software engineering techniques for the quality assurance of deep learning (DL) systems. One popular direction is DL testing—that is, given a property of test, defects of DL systems are found either by fuzzing or guided search with the help of certain testing metrics. However, recent studies have revealed that the neuron coverage metrics, which are commonly used by most existing DL testing approaches, are not necessarily correlated with model quality (e.g., robustness, the most studied model property), and are also not an effective measurement on the confidence of the model quality after testing. In this work, we address this gap by proposing a novel testing framework called QuoTe (i.e., Qu ality- o riented Te sting). A key part of QuoTe is a quantitative measurement on (1) the value of each test case in enhancing the model property of interest (often via retraining) and (2) the convergence quality of the model property improvement. QuoTe utilizes the proposed metric to automatically select or generate valuable test cases for improving model quality. The proposed metric is also a lightweight yet strong indicator of how well the improvement converged. Extensive experiments on both image and tabular datasets with a variety of model architectures confirm the effectiveness and efficiency of QuoTe in improving DL model quality—that is, robustness and fairness. As a generic quality-oriented testing framework, future adaptations can be made to other domains (e.g., text) as well as other model properties.

Sem2vec : Semantics-aware Assembly Tracelet Embedding

Binary code similarity is the foundation of many security and software engineering applications. Recent works leverage deep neural networks (DNN) to learn a numeric vector representation (namely, embeddings ) of assembly functions, enabling similarity analysis in the numeric space. However, existing DNN-based techniques capture syntactic-, control flow-, or data flow-level information of assembly code, which is too coarse-grained to represent program functionality. These methods can suffer from low robustness to challenging settings such as compiler optimizations and obfuscations. We present sem2vec , a binary code embedding framework that learns from semantics . Given the control-flow graph (CFG), 34 pages. of an assembly function, we divide it into tracelets , denoting continuous and short execution traces that are reachable from the function entry point. We use symbolic execution to extract symbolic constraints and other auxiliary information on each tracelet. We then train masked language models to compute embeddings of symbolic execution outputs. Last, we use graph neural networks, to aggregate tracelet embeddings into the CFG-level embedding for a function. Our evaluation shows that sem2vec extracts high-quality embedding and is robust against different compilers, optimizations, architectures, and popular obfuscation methods including virtualization obfuscation. We further augment a vulnerability search application with embeddings computed by sem2vec and demonstrate a significant improvement in vulnerability search accuracy.

Empathy models and software engineering — A preliminary analysis and taxonomy

Empathy is widely used in many disciplines such as philosophy, sociology, psychology, health care. Ability to empathise with software end-users seems to be a vital skill software developers should possess. This is because engineering successful software systems involves not only interacting effectively with users but also understanding their true needs. Empathy has the potential to address this situation. Empathy is a predominant human aspect that can be used to comprehend decisions, feelings, emotions and actions of users. However, to date empathy has been under-researched in software engineering (SE) context. In this position paper, we present our exploration of key empathy models from different disciplines and our analysis of their adequacy for application in SE. While there is no evidence for empathy models that are readily applicable to SE, we believe these models can be adapted and applied in SE context with the aim of assisting software engineers to increase their empathy for diverse end-user needs. We present a preliminary taxonomy of empathy by carefully considering the most popular empathy models from different disciplines. We encourage future research on empathy in SE as we believe it is an important human aspect that can significantly influence the relationship between developers and end-users.

Software Engineering Challenges for Quantum Computing

The First Working Seminar on Quantum Software Engineering (WSQSE 2022) took place on December 15 and 16, 2022 in Innsbruck, Austria. An audience of 33 quantum computing and software engineering researchers and practitioners joined the two-day event. The workshop enabled us to foresee short-term and longterms perspectives of Quantum Software Engineering, as well as a set of requirements, issues, and challenges for architecting, programming, and testing Quantum Software Engineering applications. In this report, we provide the summary of the workshop, by reporting on the structure of the event and the main results coming from the sessions and working groups.

Towards Learning Generalizable Code Embeddings Using Task-agnostic Graph Convolutional Networks

Code embeddings have seen increasing applications in software engineering (SE) research and practice recently. Despite the advances in embedding techniques applied in SE research, one of the main challenges is their generalizability. A recent study finds that code embeddings may not be readily leveraged for the downstream tasks that the embeddings are not particularly trained for. Therefore, in this article, we propose GraphCodeVec , which represents the source code as graphs and leverages the Graph Convolutional Networks to learn more generalizable code embeddings in a task-agnostic manner. The edges in the graph representation are automatically constructed from the paths in the abstract syntax trees, and the nodes from the tokens in the source code. To evaluate the effectiveness of GraphCodeVec , we consider three downstream benchmark tasks (i.e., code comment generation, code authorship identification, and code clones detection) that are used in a prior benchmarking of code embeddings and add three new downstream tasks (i.e., source code classification, logging statements prediction, and software defect prediction), resulting in a total of six downstream tasks that are considered in our evaluation. For each downstream task, we apply the embeddings learned by GraphCodeVec and the embeddings learned from four baseline approaches and compare their respective performance. We find that GraphCodeVec outperforms all the baselines in five out of the six downstream tasks, and its performance is relatively stable across different tasks and datasets. In addition, we perform ablation experiments to understand the impacts of the training context (i.e., the graph context extracted from the abstract syntax trees) and the training model (i.e., the Graph Convolutional Networks) on the effectiveness of the generated embeddings. The results show that both the graph context and the Graph Convolutional Networks can benefit GraphCodeVec in producing high-quality embeddings for the downstream tasks, while the improvement by Graph Convolutional Networks is more robust across different downstream tasks and datasets. Our findings suggest that future research and practice may consider using graph-based deep learning methods to capture the structural information of the source code for SE tasks.

Research on the Application of Software Engineering in the Construction of Enterprise Information Platforms

Research on the Application of Software Engineering in the Construction of Enterprise Information Platforms

Review of ways to apply machine learning methods in software engineering

This article reviews the integration of machine learning (ML) techniques into Software Engineering (SE) across various phases of the software development life cycle (SDLC). The purpose is to investigate the applications of ML in SE, analyze its methodologies, present findings, and draw conclusions regarding its impact. The study categorized ML applications in SE and assessed the performance of various ML algorithms. Authors identified ML applications in SDLC phases, including requirements analysis, design, implementation, testing, and maintenance. ML algorithms, such as supervised and unsupervised learning, are employed for tasks like software requirement identification, design pattern recognition, code generation, and automated testing. In summary, we find that ML-based techniques are experiencing a substantial surge in adoption within the field of software engineering. Nevertheless, it is evident that substantial endeavors are needed to establish thorough comparisons and synergies among these approaches, perform meaningful evaluations grounded in detailed real-world implementations that are applicable to industrial software development. Therefore, our key takeaway is the necessity for a shift in focus towards reproducible research, prioritizing this over isolated novel concepts. Failure to do so may result in the limited practical implementation of these promising applications.

Enhancing DNN-Based Binary Code Function Search With Low-Cost Equivalence Checking

Binary code function search has been used as the core basis of various security and software engineering applications, including malware clustering, code clone detection, and vulnerability audits. Recognizing logically similar assembly functions, however, remains a challenge. Most binary code search tools rely on program structure-level information, such as control flow and data flow graphs, that is extracted using program analysis techniques or deep neural networks (DNNs). However, DNN-based techniques capture lexical-, control structure-, or data flow-level information of binary code for representation learning, which is often too coarse-grained and does not accurately denote program functionality. Additionally, it may exhibit low robustness to a variety of challenging settings, such as compiler optimizations and obfuscations. This paper proposes a general solution for enhancing the top- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> ranked candidates in DNN-based binary code function search. The key idea is to design a low-cost and comprehensive equivalence check that quickly exposes functionality deviations between the target function and its top- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> matched functions. Functions that fail this equivalence check can be shaved from the top- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> list, and functions that pass the check can be revisited to move ahead on the top- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> ranked candidates, in a deliberate way. We design a practical and efficient equivalence check, named <monospace>BinUSE</monospace> , using <i>under-constrained</i> symbolic execution (USE). USE, a variant of symbolic execution, improves scalability by initiating symbolic execution directly from function entry points and relaxing constraints on function parameters. It eliminates the overhead incurred by path explosion and costly constraints. <monospace>BinUSE</monospace> is specifically designed to deliver an assembly function-level equivalence check, enhancing DNN-based binary code search by reducing its false alarms with low cost. Our evaluation shows that <monospace>BinUSE</monospace> can enable a general and effective enhancement of four state-of-the-art DNN-based binary code search tools when confronted with challenges posed by different compilers, optimizations, obfuscations, and architectures.

Methods for Automatic Web Page Layout Testing and Analysis: A Review

Methods for automatic analysis of user interfaces are essential for a wide range of applications in computer science and software engineering. These methods are used in software security, document archiving, human-computer interaction, software engineering, and data science. Even though these methods are essential, no single research systematically lists most of the methods and their characteristics. This paper aims to give an overview of different solutions and their applications in the separate processes of automatic analysis of user interfaces. The main focus is on the techniques that analyze web page layouts and web page structure. Web pages’ style, type of content, and even structure constantly (often drastically) change, as do methods that analyze them. The fact that most methods use very different datasets and web pages of various complexities are some of the reasons that the direct comparison of methods is difficult, if not impossible. Another fact is that the vast applications of methods practically solve similar problems. With these facts in mind, in the paper, we surveyed relevant scientific articles, categorized them, and provided an overview of how these methods have developed over time.

Investigations on visualization and interaction of ship structure multidisciplinary finite element analysis data for virtual environment

Although the extraction and sharing of important data in the finite element model (FEM) and analysis data is particularly significant in the ship structure collaborative design process, the efficiency of sharing finite element analysis (FEA) data using a standardized neutral format is still relatively low, which is attributed to the format that has to accommodate various data types generated by different commercial computer aided engineering (CAE) software and multidisciplinary analysis applications. Aiming to solve the problem of efficient visualization and sharing of heterogeneous FEA data in ship structure collaborative design and review, we proposed a new FEA data visualization framework for virtual environments (VE) to realize multidisciplinary data sharing in the collaborative design process, where only essential finite element (FE) information is stored to reduce the weight of the model and analysis data. In addition, the visualizing results can be updated in response to the modification of the model geometry and simulation variables. Furthermore, we implemented a general system based on our data framework. To maximize the efficiency of managing multidisciplinary data (statics, dynamics, ultimate strength), the system adopts a client/server (C/S) architecture and hierarchical management of data structures. The virtual visualization of these complex FEA data effectively enhances the efficiency of scientific communication, model evaluation and validation, and interdisciplinary discussion of research findings. Eventually, examples from commercial CAE software are provided to demonstrate the effectiveness and versatility of the system.

Digital medicine: expectations and prospects

The purpose of the research is to systematize the problems of the branch, formulate goals and objectives, search for methods and software and organizational solutions for digital medicine. Applied systems analysis, software engineering and artificial intelligence methods are used as research methods. The result of the research is the digital platform “Health History”, an organizational and technical system, which is a working prototype of a system solution for the reorganization of processes. The conclusion is postulated about the need for fundamental organizational changes in medicine based on information technologies, which is the essence of the digital transformation of healthcare.

A survey of deep learning based text-to-SQL generation

Text-to-SQL is an important application of automation software engineering. It is also a research hotspot in the field of semantic parsing. The text-to-SQL task aims to automatically generate the SQL statement according to the natural language description. It allows nonprofessionals to access the database without understanding SQL syntax. With the development of large-scale text-to-SQL datasets and artificial intelligence technologies, the text-to-SQL task is also making great progress. Compared with the traditional text-to-SQL generation, the deep learning-based text-to-SQL has the advantages of high accuracy, flexibility, and iterative learning. In recent years, several studies have focused on SQL generation based on deep learning. This research summarizes existing works from the aspects of text-to-SQL scenarios, datasets, model structures, and evaluation methods.