Software Engineering Perspective Research Articles

Fatal Software Failures in Spaceflight

Space exploration has become an integral part of modern society, and since its early days in the 1960s, software has grown in importance, becoming indispensable for spaceflight. However, software is boon and bane: while it enables unprecedented functionality and cost reductions and can even save spacecraft, its importance and fragility also make it a potential Achilles heel for critical systems. Throughout the history of spaceflight, numerous accidents with significant detrimental impacts on mission objectives and safety have been attributed to software, although unequivocal attribution is sometimes difficult. In this Entry, we examine over two dozen software-related mishaps in spaceflight from a software engineering perspective, focusing on major incidents and not claiming completeness. This Entry article contextualizes the role of software in space exploration and aims to preserve the lessons learned from these mishaps. Such knowledge is crucial for ensuring future success in space endeavors. Finally, we explore prospects for the increasingly software-dependent future of spaceflight.

Looking back and forward: A retrospective and future directions on software engineering for systems‐of‐systems

AbstractModern systems are increasingly connected and more integrated with other existing systems, giving rise to systems‐of‐systems (SoS). An SoS consists of a set of independent, heterogeneous systems that interact to provide new functionalities and accomplish global missions through emergent behavior manifested at runtime. The distinctive characteristics of SoS, when contrasted to traditional systems, pose significant research challenges within software engineering. These challenges motivate the need for a paradigm shift and the exploration of novel approaches for designing, developing, deploying, and evolving these systems. The International Workshop on Software Engineering for Systems‐of‐Systems (SESoS) series started in 2013 to fill a gap in scientific forums addressing SoS from the software engineering perspective, becoming the first venue for this purpose. This article presents a study aimed at outlining the evolution and future trajectory of software engineering for SoS based on the examination of 57 papers spanning the 11 editions of the SESoS workshop (2013–2023). The study combined scoping review and scientometric analysis methods to categorize and analyze the research contributions concerning temporal and geographic distribution, topics of interest, research methodologies employed, application domains, and research impact. Based on such a comprehensive overview, this article discusses current and future directions in software engineering for SoS.

Design of college educational management system based on CS and BS hybrid architecture

In order to improve the efficiency of the educational administration system in colleges and universities, a technology based on the hybrid architecture of C/S and B/S is proposed. Based on the development experience of previous information management systems, this method analyzes and compares the commonly used C/S architecture and B/S architecture. From the perspective of software engineering, the design idea of genetic algorithm course scheduling management system based on C/S and B/S cross-parallel mode is proposed. The experimental results show that the course goodness analysis method in the class schedule arrangement scheme using genetic algorithm shows that the average course goodness value is 3.2, which is not greater than the weighted total goodness value of 0.4. The feasibility of the design idea of the genetic algorithm class scheduling management system based on the C/S and B/S cross-parallel mode is effectively proved. It is concluded that it is proved that the technology research based on the hybrid architecture of C/S and B/S can effectively improve the effect of the educational administration system in colleges and universities.

From modeling and simulation to Digital Twin: evolution or revolution?

As digitalization is permeating all sectors of society toward the concept of “smart everything,” and virtual technologies and data are gaining a dominant place in the engineering and control of intelligent systems, the Digital Twin (DT) concept has surfaced as one of the top technologies to adopt. This paper discusses the DT concept from the viewpoint of Modeling and Simulation (M&S) experts. It both provides literature review elements and adopts a commentary-driven approach. We first examine the DT from a historical perspective, tracing the historical development of M&S from its roots in computational experiments to its applications in various fields and the birth of DT-related and allied concepts. We then approach DTs as an evolution of M&S, acknowledging the overlap in these different concepts. We also look at the M&S workflow and its evolution toward a DT workflow from a software engineering perspective, highlighting significant changes. Finally, we look at new challenges and requirements DTs entail, potentially leading to a revolutionary shift in M&S practices. In this way, we hope to foster the discussion on DTs and provide the M&S expert with innovative perspectives.

What is an app store? The software engineering perspective

What is an app store? The software engineering perspective

The Impact of Resource Allocation on the Machine Learning Lifecycle

AbstractAn organization’s ability to develop Machine Learning (ML) applications depends on its available resource base. Without awareness and understanding of all relevant resources as well as their impact on the ML lifecycle, we risk inefficient allocations as well as missing monopolization tendencies. To counteract these risks, the study develops a framework that interweaves the relevant resources with the procedural and technical dependencies within the ML lifecycle. To rigorously develop and evaluate this framework the paper follows the Design Science Research paradigm and builds on a literature review and an interview study. In doing so, it bridges the gap between the software engineering and management perspective to advance the ML management discourse. The results extend the literature by introducing not yet discussed but relevant resources, describing six direct and indirect effects of resources on the ML lifecycle, and revealing the resources’ contextual properties. Furthermore, the framework is useful in practice to support organizational decision-making and contextualize monopolization tendencies.

Rise of Distributed Deep Learning Training in the Big Model Era: From a Software Engineering Perspective

Deep learning (DL) has become a key component of modern software. In the “ big model ” era, the rich features of DL-based software (i.e., DL software) substantially rely on powerful DL models, e.g., BERT, GPT-3, and the recently emerging GPT-4, which are trained on the powerful cloud with large datasets. Hence, training effective DL models has become a vital stage in the whole software lifecycle. When training deep learning models, especially those big models, developers need to parallelize and distribute the computation and memory resources amongst multiple devices (e.g., a cluster of GPUs) in the training process, which is known as distributed deep learning training , or distributed training for short. However, the unique challenges that developers encounter in distributed training process have not been studied in the software engineering community. Given the increasingly heavy dependence of current DL-based software on distributed training, this paper aims to fill in the knowledge gap and presents the first comprehensive study on developers’ issues in distributed training. To this end, we focus on popular DL frameworks that support distributed training (including TensorFlow, PyTorch, Keras, and Horovod) and analyze 1,131 real-world developers’ issues about using these frameworks reported on Stack Overflow and GitHub. We construct a fine-grained taxonomy consisting of 30 categories regarding the fault symptoms and summarize common fix patterns for different symptoms. We find that: (1) many distributed-specific faults and non-distributed-specific faults inherently share the same fault symptoms, making it challenging to debug; (2) most of the fault symptoms have frequent fix patterns; (3) about half of the faults are related to system-level configurations. Based on the results, we suggest actionable implications on research avenues that can potentially facilitate the distributed training to develop DL-based software, such as focusing on the frequent and common fix patterns when designing testing or debugging tools, developing efficient testing and debugging techniques for communication configuration along with the synthesis of network configuration analysis, designing new multi-device checkpoint-and-replay techniques to help reproduction, and designing serverless APIs for cloud platforms.

Exploring knowledge management from a software engineering perspective

Context: The knowledge domain of software engineering (SE) gradually expands due to fast emerging technologies and complex organisational processes. The software industry represents a special focus of interest from a Knowledge Management (KM) perspective, as it is characterized by high employee turnover, a high level of outsourced projects, and an accelerated pace of technology innovations. Knowledge management can substantially influence SE knowledge's life cycle, contributing to better structuring of organisational processes of knowledge creation, knowledge transfer, and knowledge sharing. Reflecting on the trends for SE automation with artificial intelligence tools, KM approaches can enable companies and professionals to reconsider the future of SE work. Purpose: The present research aims to identify and investigate how implementing knowledge management principles and processes within the software engineering domain can facilitate the future transformation of software-related jobs. Based on a conceptual model, built upon the literature review, the paper analyses the quantitative data, collected from 91 software experts. Evaluating the impact of both organizational and domain types of SE knowledge, the discussion evaluates the unique value-adding SE processes along with models of how to explore AI applications to automate the SE domain. Results: The outcomes of the analysis identify the models of structuring knowledge management processes in SE both within the technological domain and organisational layers. The discussion section establishes suitable KM methods and approaches for supporting SE job transformation. It identifies the main knowledge management integration practices, demonstrating how combining KM with AI-based innovations may change the SE knowledge flow, improving substantially SE activities like onboarding new team members, monitoring software documentation, version control, and error tracking.

Advancing database security: a comprehensive systematic mapping study of potential challenges

The value of data to a company means that it must be protected. When it comes to safeguarding their local and worldwide databases, businesses face a number of challenges. To systematically review the literature to highlight the difficulties in establishing, implementing, and maintaining secure databases. In order to better understand database system problems, we did a systematic mapping study (SMS). We’ve analyzed 100 research publications from different digital libraries and found 20 issues after adopting inclusion and exclusion criteria. This SMS study aimed to identify the most up-to-date research in database security and the different challenges faced by users/clients using various databases from a software engineering perspective. In total, 20 challenges were identified related to database security. Our results show that “weak authorization system”, “weak access control”, “privacy issues/data leakage”, “lack of NOP security”, and “database attacks” as the most frequently cited critical challenges. Further analyses were performed to show different challenges with respect to different phases of the software development lifecycle, venue of publications, types of database attacks, and active research institutes/universities researching database security. The organizations should implement adequate mitigation strategies to address the identified database challenges. This research will also provide a direction for new research in this area.

Symbolic knowledge injection meets intelligent agents: QoS metrics and experiments

Bridging intelligent symbolic agents and sub-symbolic predictors is a long-standing research goal in AI. Among the recent integration efforts, symbolic knowledge injection (SKI) proposes algorithms aimed at steering sub-symbolic predictors’ learning towards compliance w.r.t. pre-existing symbolic knowledge bases. However, state-of-the-art contributions about SKI mostly tackle injection from a foundational perspective, often focussing solely on improving the predictive performance of the sub-symbolic predictors undergoing injection. Technical contributions, in turn, are tailored on individual methods/experiments and therefore poorly interoperable with agent technologies as well as among each others. Intelligent agents may exploit SKI to serve many purposes other than predictive performance alone—provided that, of course, adequate technological support exists: for instance, SKI may allow agents to tune computational, energetic, or data requirements of sub-symbolic predictors. Given that different algorithms may exist to serve all those many purposes, some criteria for algorithm selection as well as a suitable technology should be available to let agents dynamically select and exploit the most suitable algorithm for the problem at hand. Along this line, in this work we design a set of quality-of-service (QoS) metrics for SKI, and a general-purpose software API to enable their application to various SKI algorithms—namely, platform for symbolic knowledge injection (PSyKI). We provide an abstract formulation of four QoS metrics for SKI, and describe the design of PSyKI according to a software engineering perspective. Then we discuss how our QoS metrics are supported by PSyKI. Finally, we demonstrate the effectiveness of both our QoS metrics and PSyKI via a number of experiments, where SKI is both applied and assessed via our proposed API. Our empirical analysis demonstrates both the soundness of our proposed metrics and the versatility of PSyKI as the first software tool supporting the application, interchange, and numerical assessment of SKI techniques. To the best of our knowledge, our proposals represent the first attempt to introduce QoS metrics for SKI, and the software tools enabling their practical exploitation for both human and computational agents. In particular, our contributions could be exploited to automate and/or compare the manifold SKI algorithms from the state of the art. Hence moving a concrete step forward the engineering of efficient, robust, and trustworthy software applications that integrate symbolic agents and sub-symbolic predictors.

Review of the Research Progress in Combat Simulation Software

To address the new functional requirements brought by the introduction of new weapons and new combat modes, a comprehensive survey of the research progress in the area of combat simulation software is performed from the perspective of software engineering. First, the top-level specification, simulation engine, and simulation framework of combat simulation software are reviewed. Then, several typical combat simulation software systems are demonstrated, and the relevant software frameworks are analyzed. Finally, combining the application prospect of artificial intelligence, metaverse, and other new technologies in combat simulation, the development trends of combat simulation software are presented, namely intellectualization, adaptation to an LVC (live, virtual, and constructive) system, and a more game-based experience. Based on a comprehensive comparison between the mentioned simulation frameworks, we believe that the AFSIM (Advanced framework for simulation, integration, and modeling) and the E-CARGO (Environments—classes, agents, roles, groups, and objects) are appropriate candidates for developing distributed combat simulation software.

An unsupervised learning-based methodology for uncovering behavioural patterns for specific types of software defects

Software deffect prediction, a problem of major relevance within the search-based software engineering field, aims to enhance software quality by early and precisely uncovering faulty software modules. Accurate detection of software defects in new software releases might increase the performance of the software development process in terms of cost, time and software quality. Most approaches from the software deffect prediction literature try to develop general solutions that are designed to work with any type of software deffect. From a software engineering perspective, software defects may take various forms and identifying/fixing different types of defects requires different approaches. Starting from the assumption that specific types of software defects have a particular behaviour, we are introducing in this paper, as a proof of concept, an unsupervised learning-based methodology for mining behavioural patterns for specific classes of software defects and identifying features which would be relevant for detecting the uncovered classes. The experiments performed on an open-source software deffect prediction data set collected from all releases of the Apache Ivy software highlight that the results obtained by applying the proposed methodology are highly correlated with the way human domain experts categorise and address software defects. Creating software deffect prediction models that are specifically tailored for different software deffect types may improve the accuracy of the developed models, open the possibility to apply different sets of predictive models based on the domain of the software and may accelerate the adoption of software deffect prediction approaches by the industry.

Handling multiple levels in agent-based models of complex socio-environmental systems: A comprehensive review

Agent-based modeling (ABM) has been successfully used, since its emergence in the 1990s, to model and simulate the dynamics at work in complex socio-environmental systems, in many domains and applications where interactions between people and their environments give rise to emergent phenomena that are difficult to study otherwise (urban planning, land-use change, adaptation to environmental changes, biodiversity protection in socio-ecosystems, environmental pollution control, etc.). The inclusion of multiple levels of analysis, abstraction, and representation in these models, however, is much more recent and is still the subject of many proposals and discussions within a relatively informal field, Multilevel Agent-Based Modeling (ML-ABM), which is most often presented as an approach that extends the classical ABM paradigm to include multilevel concepts. Over the past decade, ML-ABM has been increasingly adopted and explored by researchers as an effective paradigm for framing and defining the mechanisms underlying multilevel dynamics. However, due to the youth of the field, no single definition, methodology, or tool unifies studies in this rapidly expanding area. This review will begin with an introduction to socio-environmental systems (SES) and the challenges that modeling approaches face in representing them properly, especially regarding the complexity of human behaviors and organizations. ABM presents opportunities for modeling SESs with respect to these challenges, including the simulation of individual and social behavior and their ability to provide a descriptive and generative representation of the simulated system. However, ABM is limited in its ability to represent levels and scales, as these concepts are absent from the classical ABM metamodel. A complete review of the ML-ABM literature will be carried out, structured around a continuum that emerged during the review: that of the distribution of behaviors (and thus, from a software engineering perspective, of control) across the levels, from approaches that allow only one level to be active at a time, to approaches that rely on simultaneous activity and feedback loops between several levels. Different design choices will, thus, be presented to meet the different needs of multi-level representation, focusing on the interest on modelers and the strengths and limitations of each. In particular, we will highlight a limitation shared by all the reviewed approaches, namely their inability to represent several parallel hierarchies of levels and their interactions, a capability that appears more and more crucial to finely represent social behaviors in SES. A new perspective on the interest that the AGR approach could represent to allow this representation of hierarchies allows us to conclude on the research perspectives are still open.

An Effective Software Architecture of Islamic Inheritance System Employing Structured Paradigm

Distribution of wealth of a deceased person among the heirs is a critical and complicated problem in Islamic jurisprudence system. In a Muslim society, the demand of a wealth calculation system is very high and plays a pivotal role in day-to-day life of Muslim families. In this paper, we propose a structured and well-defined software solution that employs structured software engineering approach using Data Flow Diagram (DFD), structure chart and program description language (PDL). The proposed system distributes shares among the heirs based on Islamic rules. Many such systems have been built by different govt agencies of different Muslim majority countries. Many of those systems are error prone and are not well defined from the software engineering perspective. In many cases, the software architecture of such systems is undisclosed and their applicability in many complex deceased-heir relationship provides wrong calculation. The proposed solution on the other hand, makes the successful use of software engineering approach to interpret Islamic Inheritance rules in a more manageable, scalable and maintainable fashion. The system is rigorously tested on different complex scenarios and the results are verified by Muslim family lawyers. The system is developed using C programming language and we make the source code open.

Game Development Topics: A Tag-Based Investigation on Game Development Stack Exchange

Video-game development, despite being a multi-billion-dollar industry, has not attracted sustained attention from software engineering researchers and remains understudied from a software engineering perspective. We aim to uncover, from game developers’ perspectives, which video game development topics are the most asked about and which are the most supported, in order to provide insights about technological and conceptual challenges game developers and managers may face on their projects. To do so, we turned to the Game Development Stack Exchange (GDSE), a prominent Question and Answer forum dedicated to game development. On that forum, users ask questions and tag them with keywords recognized as important categories by the community. Our study relies on those tags, which we classify either as technology or concept topics. We then analysed these topics for their levels of community attention (number of questions, views, upvotes, etc.) and community support (whether their questions are answered and how long it takes). Related to community attention, we found that topics with the most questions include concepts such as 2D and collision detection and technologies such as Unity and C#, whereas questions touching on concepts such as video and augmented reality and technologies such as iOS, Unreal-4 and Three.js generally lack satisfactory answers. Moreover, by pairing topics, we uncovered early clues that, from a community support perspective, (i) the pairing of some technologies appear more challenging (e.g., questions mixing HLSL and MonoGame receive a relatively lower level of support); (ii) some concepts may be more difficult to handle conjointly (e.g., rotation and movement); and some technologies may prove more challenging to use to address a given concept (e.g., Java for 3D). Our findings provide insights to video game developers on the topics and challenges they might encounter and highlight tool selection and integration for video game development as a promising research direction.

A Model-Driven Methodology to Accelerate Software Engineering in the Internet of Things

The Internet of Things (IoT) aims for connecting. This assumption brings about several software engineering challenges that constitute a serious obstacle to its wider adoption. The main feature of the IoT is genericity w.r.t the variability of software and hardware technologies. Model-driven engineering (MDE) is a paradigm that advocates using models to address software engineering problems. It can help to meet the genericity of the IoT from a software engineering perspective. Existing MDE approaches for the IoT focus only on modeling the internal behavior of things but lack a comprehensive approach dedicated to network modeling. In the present article, we introduce a network-oriented methodology based on MDE to unify the IoT’s heterogeneous concepts. Fundamentally, we avoid the intrinsic heterogeneity of the IoT by separating the network’s specification (i.e., the things, the communication scheme, and its constraints) from its concrete implementation (i.e., the low-level artifacts, such as source code and documentation). Technically, the methodology relies on a model-based domain-specific language (DSL) and a code generator. The former enables the modeling of the network’s specification, and the latter provides a procedure to generate the low-level artifacts from this specification. Our results show that this methodology makes iot’s software engineering more rigorous, helps prevent bugs earlier, and saves time.

A Cross-Domain Systematic Mapping Study on Software Engineering for Digital Twins

Digital Twins are currently investigated as the technological backbone for providing an enhanced understanding and management of existing systems as well as for designing new systems in various domains, e.g., ranging from single manufacturing components such as sensors to large-scale systems such as smart cities. Given the diverse application domains of Digital Twins, it is not surprising that the characterization of the term Digital Twin, as well as the needs for developing and operating Digital Twins are multi-faceted. Providing a better understanding what the commonalities and differences of Digital Twins in different contexts are, may allow to build reusable support for developing, running, and managing Digital Twins by providing dedicated concepts, techniques, and tool support. In this paper, we aim to uncover the nature of Digital Twins based on a systematic mapping study which is not limited to a particular application domain or technological space. We systematically retrieved a set of 1471 unique publications of which 356 were selected for further investigation. In particular, we analyzed the types of research and contributions made for Digital Twins, the expected properties Digital Twins have to fulfill, how Digital Twins are realized and operated, as well as how Digital Twins are finally evaluated. Based on this analysis, we also contribute a novel feature model for Digital Twins from a software engineering perspective as well as several observations to further guide future software engineering research in this area.

Functional and Technical Aspects of Self-management mHealth Apps: Systematic App Search and Literature Review.

BackgroundAlthough the past decade has witnessed the development of many self-management mobile health (mHealth) apps that enable users to monitor their health and activities independently, there is a general lack of empirical evidence on the functional and technical aspects of self-management mHealth apps from a software engineering perspective.ObjectiveThis study aims to systematically identify the characteristics and challenges of self-management mHealth apps, focusing on functionalities, design, development, and evaluation methods, as well as to specify the differences and similarities between published research papers and commercial and open-source apps.MethodsThis research was divided into 3 main phases to achieve the expected goal. The first phase involved reviewing peer-reviewed academic research papers from 7 digital libraries, and the second phase involved reviewing and evaluating apps available on Android and iOS app stores using the Mobile Application Rating Scale. Finally, the third phase involved analyzing and evaluating open-source apps from GitHub.ResultsIn total, 52 research papers, 42 app store apps, and 24 open-source apps were analyzed, synthesized, and reported. We found that the development of self-management mHealth apps requires significant time, effort, and cost because of their complexity and specific requirements, such as the use of machine learning algorithms, external services, and built-in technologies. In general, self-management mHealth apps are similar in their focus, user interface components, navigation and structure, services and technologies, authentication features, and architecture and patterns. However, they differ in terms of the use of machine learning, processing techniques, key functionalities, inference of machine learning knowledge, logging mechanisms, evaluation techniques, and challenges.ConclusionsSelf-management mHealth apps may offer an essential means of managing users’ health, expecting to assist users in continuously monitoring their health and encourage them to adopt healthy habits. However, developing an efficient and intelligent self-management mHealth app with the ability to reduce resource consumption and processing time, as well as increase performance, is still under research and development. In addition, there is a need to find an automated process for evaluating and selecting suitable machine learning algorithms for the self-management of mHealth apps. We believe that these issues can be avoided or significantly reduced by using a model-driven engineering approach with a decision support system to accelerate and ameliorate the development process and quality of self-management mHealth apps.

A Taxonomy for Characterizing Blockchain Systems

Blockchain is a promising technology that has received extensive attention from academia and industry in various subject domains. The significant potential of blockchain technology lies in its key characteristics of decentralization, persistency, anonymity, and auditability. However, the development of blockchain applications faces many technical difficulties because such applications are highly complex and heterogeneous, involving cooperation and interoperation between legacy systems and blockchain systems. These difficulties are further exacerbated by the lack of a common understanding of different blockchain solutions and components from a software engineering perspective. This study proposes a taxonomy to classify the eight fundamental components of blockchain systems. These components comprise a platform, network, distributed ledger, smart contract, consensus protocol, digital wallet, token, and node. Under the proposed taxonomy, each component is divided into several aspects (i.e., characteristics) that collectively describe the component. As a result of this elaboration, the taxonomy organizes the eight components into 83 dimensions and 199 characteristics. The taxonomy was validated using illustrative scenarios and a case study. The proposed taxonomy aims to provide a better understanding of the interrelationships among different blockchain components and aspects. Such an understanding can help blockchain application developers to design better blockchain architectures and systems.

The Internet of Things and Architectures of Big Data Analytics: Challenges of Intersection at Different Domains

The current exponential advancements in the Internet of Things (IoT) technologies pave a vast intelligent computing platform by integrating smart objects with sensing, processing and communication capabilities. The core element of IoT is the complex big data generated from different interconnected sources at real-time, presenting divergent processing and analysis challenges. Best practices in software engineering have been continuously addressed in IoT technologies to handle such big data efficiently at different domains. Despite of the massive studies dedicated for IoT, no explicit processing architecture is proposed based on real investigation of software engineering concepts and big data analytics characteristics in IoT. This paper provides a systematic literature review for the current state-of-the-art of IoT systems in different domains. The study investigates the current techniques and technologies that serve IoT systems from the big data analytics and software engineering perspectives, revealing a matrix for the specific IoT data features and their encountered challenges and gaps for each domain. The review deduces a proposed domain-independent software architecture for big IoT data analytics, maintaining various IoT data processing challenges, including data scalability, timeliness, heterogeneity, inconsistency, confidentiality and correlations. Finally, the main research gaps are emphasized for future considerations.