Software Defect Prediction Method Based on Stable Learning

The purpose of software defect prediction is to identify defect-prone code modules to assist software quality assurance teams with the appropriate allocation of resources and labor.In previous software defect prediction studies,transfer learning was effective in solving the problem of inconsistent project data distribution.However,target projects often lack sufficient data,which affects the performance of the transfer learning model.In addition,the presence of uncorrelated features between projects can decrease the prediction accuracy of the transfer learning model.To address these problems,this article propose a software defect prediction method based on stable learning(SDP-SL)that combines code visualization techniques and residual networks.This method first transforms code files into code images using code visualization techniques and then constructs a defect prediction model based on these code images.During the model training process,target project data are not required as prior knowledge.Following the principles of stable learning,this paper dynamically adjusted the weights of source project samples to eliminate dependencies between features,thereby capturing the“invariance mechanism”within the data.This approach explores the genuine relationship between code defect features and labels,thereby enhancing defect prediction performance.To evaluate the performance of SDP-SL,this article conducted comparative experiments on 10 open-source projects in the PROMISE dataset.The experimental results demonstrated that in terms of the F-measure,the proposed SDP-SL method outperformed other within-project defect prediction methods by 2.11%-44.03%.In cross-project defect prediction,the SDP-SL method provided an improvement of 5.89%-25.46% in prediction performance compared to other cross-project defect prediction methods.Therefore,SDP-SL can effectively enhance within-and cross-project defect predictions.

肝硬化并SBP患者外周血SAA、WBC/PLT、CD64及腹水CD64联合检测的意义

目的 分析肝硬化并自发性细菌性腹膜炎(SBP)外周血淀粉样蛋白A(SAA)、白细胞计数(WBC)/血小板(PLT)、白细胞分化抗原64(CD64)及腹水CD64联合检测的意义。方法 2021年1月至2022年12月成都中医药大学附属医院收治的106例肝硬化并腹水患者,根据是否发生SBP分为SBP组49例和非SBP组57例,选取同期50名健康人为对照组。免疫透射比浊法测定外周血SAA,全自动血液分析仪测定WBC、PLT,流式细胞仪测定外周血及腹水CD64。比较3组外周血SAA、WBC/PLT、CD64及腹水CD64,并通过绘制受试者工作特征曲线(ROC)评价外周血SAA、WBC/PLT、CD64及腹水CD64对肝硬化并SBP的诊断效能。结果 SBP组外周血SAA、WBC/PLT、CD64及腹水CD64分别为(82.3±17.2)mg/mL、(0.4±0.2)、(13 126.3±90.1)mol/cell、(14 912.8±101.7)mol/cell,高于非SBP组的(11.2±3.4)mg/mL、(0.1±0.1)、(1083.9±61.4)mol/cell、(936.2±51.1)mol/cell,(P<0.05);SBP组外周血SAA、WBC/PLT、CD64显著高于对照组的(10.6±2.8)mg/mL、(0.1±0.1)、(1063.7±57.5)mol/cell,(P<0.05)。非SBP组与对照组外周血SAA、WBC/PLT、CD64比较差异无统计学意义(P>0.05)。SBP组治疗后外周血SAA、WBC/PLT、CD64及腹水CD64分别为(35.6±6.9)mg/mL、(0.3±0.1)、(3302.4±70.5)mol/cell、(4758.4±78.6)mol/cell,显著低于SBP组治疗前(P<0.05)。ROC曲线分析显示,外周血SAA、WBC/PLT、CD64及腹水CD64联合诊断肝硬化并SBP的敏感度为96.00%,AUC为0.930,均高于单独检测(P<0.05)。结论 肝硬化并SBP患者外周血SAA、WBC/PLT、CD64及腹水CD64异常增高,且联合检测对肝硬化并SBP的早期诊断价值高。

灵宝杜鹃SBP基因家族鉴定及其在非生物胁迫下的表达分析

【目的】SQUAMOSA启动子结合蛋白(SBP)家族在植物发育和非生物胁迫等方面起重要作用,该研究可为灵宝杜鹃SBP基因家族成员的生物学功能和在非生物胁迫响应的调控功能提供参考,并为灵宝杜鹃物种保护奠定基础。【方法】用生物信息学方法对灵宝杜鹃(Rhododendron henanense subsp.lingbaoense)SBP基因家族成员进行全基因组鉴定和分析,基于转录组数据分析组织特异性,并用qRT-PCR方法检测SBP在非生物胁迫下的表达。【结果】(1)从灵宝杜鹃基因组中共鉴定出19个含SBP保守结构域SBP成员,依次命名为RhlSBP1-RhlSBP19,基因不均匀分布在8条染色体上,编码的氨基酸长度为179~1072 aa,分子质量为20.26~118.73 kD;蛋白定位于细胞核;(2)系统发育分析将19个RhlSBP分为5个亚族,大部分RhlSBP与拟南芥AtSPL家族成员聚类;(3)所有的RhlSBP共有motif 1、motif 2和motif 4,基因结构显示Ⅳ、Ⅴ亚族内含子数量远多于Ⅰ、Ⅱ、Ⅲ亚族;(4)RhlSBP启动子中含有大量光响应元件、激素响应元件和环境胁迫响应元件,推测RhlSBP可能在灵宝杜鹃响应光调控、环境胁迫中发挥重要作用;(5)灵宝杜鹃与其同科植物羊踟蹰、圆叶杜鹃的SBP基因有更高的共线性;(6)RhlSBP基因亚家族成员具有相似的组织表达模式;(7)RhlSBP基因对非生物胁迫具有响应作用,但不同成员在不同胁迫下的响应程度存在差异。茉莉酸甲酯和低温处理后,基因表达总体呈上调趋势,高盐会抑制RhlSBP基因表达,干旱条件下,RhlSBP基因表达呈先上调再下调趋势。【结论】RhlSBP1、RhlSBP8可能是干旱、低温和MeJA诱导关键基因。

莴苣SBP转录因子基因家族鉴定及其在茎用莴苣茎膨大期的表达分析

SBP家族基因在植物生长发育过程中具有重要作用。为明确茎用莴苣SBP家族基因染色体分布、启动子顺式作用元件、茎膨大期表达特征及其编码蛋白质的理化性质,本研究以拟南芥SBP蛋白氨基酸序列为参考筛选鉴定莴苣SBP家族基因,进行莴苣SBP家族基因的染色体定位、进化树构建、启动子顺式作用元件分析,构建其编码蛋白质的互作网络,并利用转录组测序技术及实时荧光定量PCR分析茎用莴苣茎膨大期SBP家族基因的表达模式。结果表明,在莴苣基因组中共鉴定到27个SBP基因,可分为7个亚族,不均匀分布于8条染色体上。多个SBP转录因子与植物开花相关蛋白LFY、乙烯响应转录因子(TOE2、TOE3、SMZ、SNZ)存在互作。在茎用莴苣茎膨大过程中,13个SBP基因的表达模式存在差异。随着茎用莴苣茎的膨大,LsSBP25的表达量呈增加趋势,而LsSBP10、LsSBP22的表达量逐步降低。本研究结果可为进一步探究莴苣SBP家族基因在莴苣生长发育过程中的作用提供基础。

高粱SBP基因家族鉴定及干旱胁迫下的表达模式分析

在高等植物中,SQUAMOSA Promoter Binding Protein-Box(SBP)普遍参与植物的生长、开花调控及细胞程序性死亡等过程。本研究对高粱(Sorghum bicolor L.)SBP转录因子家族进行鉴定并进行生物信息学分析,同时利用qRT-PCR对高粱SBP基因在PEG-6000模拟干旱胁迫下的表达进行分析,以验证其功能。结果表明,从高粱中共鉴定出19个SBP成员,除第8条染色体外,其他9条染色体上均分布有SBP基因;绝大部分SBP蛋白在亚细胞水平定位于细胞核,且均为亲水性蛋白质。系统发育分析结果表明,与水稻类似,这19个高粱SBP基因也可分为3个亚类,分别含有7、5、7个SBP基因;相比于拟南芥,高粱SBP与水稻、玉米的SBP关系更近。启动子顺式作用元件预测结果显示,高粱SBP可能参与了脱落酸、茉莉酸甲酯信号通路,部分成员可能与植物的低温响应、昼夜节律调控有关。qRT-PCR分析结果显示,高粱SBP基因的表达具有组织特异性,有17个基因受干旱胁迫诱导上调表达,且随胁迫时间的延长存在差异化表达模式。本研究结果可为后续研究高粱SBP基因的功能提供理论依据。

Open-Source Software Defined Networking Controllers:State-of-the-Art,Challenges and Solutions for Future Network Providers

Software Defined Networking(SDN)is programmable by separation of forwarding control through the centralization of the controller.The controller plays the role of the‘brain’that dictates the intelligent part of SDN technology.Various versions of SDN controllers exist as a response to the diverse demands and functions expected of them.There are several SDN controllers available in the open market besides a large number of commercial controllers;some are developed tomeet carrier-grade service levels and one of the recent trends in open-source SDN controllers is the Open Network Operating System(ONOS).This paper presents a comparative study between open source SDN controllers,which are known as Network Controller Platform(NOX),Python-based Network Controller(POX),component-based SDN framework(Ryu),Java-based OpenFlow controller(Floodlight),OpenDayLight(ODL)and ONOS.The discussion is further extended into ONOS architecture,as well as,the evolution of ONOS controllers.This article will review use cases based on ONOS controllers in several application deployments.Moreover,the opportunities and challenges of open source SDN controllers will be discussed,exploring carriergrade ONOS for future real-world deployments,ONOS unique features and identifying the suitable choice of SDN controller for service providers.In addition,we attempt to provide answers to several critical questions relating to the implications of the open-source nature of SDN controllers regarding vendor lock-in,interoperability,and standards compliance,Similarly,real-world use cases of organizations using open-source SDN are highlighted and how the open-source community contributes to the development of SDN controllers.Furthermore,challenges faced by open-source projects,and considerations when choosing an open-source SDN controller are underscored.Then the role of Artificial Intelligence(AI)and Machine Learning(ML)in the evolution of open-source SDN controllers in light of recent research is indicated.In addition,the challenges and limitations associated with deploying open-source SDN controllers in production networks,how can they be mitigated,and finally how opensource SDN controllers handle network security and ensure that network configurations and policies are robust and resilient are presented.Potential opportunities and challenges for future Open SDN deployment are outlined to conclude the article.

Static Analysis Techniques for Fixing Software Defects in MPI-Based Parallel Programs

The Message Passing Interface (MPI) is a widely accepted standard for parallel computing on distributed memorysystems.However, MPI implementations can contain defects that impact the reliability and performance of parallelapplications. Detecting and correcting these defects is crucial, yet there is a lack of published models specificallydesigned for correctingMPI defects. To address this, we propose a model for detecting and correcting MPI defects(DC_MPI), which aims to detect and correct defects in various types of MPI communication, including blockingpoint-to-point (BPTP), nonblocking point-to-point (NBPTP), and collective communication (CC). The defectsaddressed by the DC_MPI model include illegal MPI calls, deadlocks (DL), race conditions (RC), and messagemismatches (MM). To assess the effectiveness of the DC_MPI model, we performed experiments on a datasetconsisting of 40 MPI codes. The results indicate that the model achieved a detection rate of 37 out of 40 codes,resulting in an overall detection accuracy of 92.5%. Additionally, the execution duration of the DC_MPI modelranged from 0.81 to 1.36 s. These findings show that the DC_MPI model is useful in detecting and correctingdefects in MPI implementations, thereby enhancing the reliability and performance of parallel applications. TheDC_MPImodel fills an important research gap and provides a valuable tool for improving the quality ofMPI-basedparallel computing systems.

Towards Improving the Quality of Requirement and Testing Process in Agile Software Development:An Empirical Study

Software testing is a critical phase due to misconceptions about ambiguities in the requirements during specification,which affect the testing process.Therefore,it is difficult to identify all faults in software.As requirement changes continuously,it increases the irrelevancy and redundancy during testing.Due to these challenges;fault detection capability decreases and there arises a need to improve the testing process,which is based on changes in requirements specification.In this research,we have developed a model to resolve testing challenges through requirement prioritization and prediction in an agile-based environment.The research objective is to identify the most relevant and meaningful requirements through semantic analysis for correct change analysis.Then compute the similarity of requirements through case-based reasoning,which predicted the requirements for reuse and restricted to error-based requirements.Afterward,the apriori algorithm mapped out requirement frequency to select relevant test cases based on frequently reused or not reused test cases to increase the fault detection rate.Furthermore,the proposed model was evaluated by conducting experiments.The results showed that requirement redundancy and irrelevancy improved due to semantic analysis,which correctly predicted the requirements,increasing the fault detection rate and resulting in high user satisfaction.The predicted requirements are mapped into test cases,increasing the fault detection rate after changes to achieve higher user satisfaction.Therefore,the model improves the redundancy and irrelevancy of requirements by more than 90%compared to other clustering methods and the analytical hierarchical process,achieving an 80%fault detection rate at an earlier stage.Hence,it provides guidelines for practitioners and researchers in the modern era.In the future,we will provide the working prototype of this model for proof of concept.

HV Process Model of Software Development

Software Development Life Cycle (SDLC) is one of the major ingredients for the development of efficient software systems within a time frame and low-cost involvement. From the literature, it is evident that there are various kinds of process models that are used by the software industries for the development of small, medium and long-term software projects, but many of them do not cover risk management. It is quite obvious that the improper selection of the software development process model leads to failure of the software products as it is time bound activity. In the present work, a new software development process model is proposed which covers the risks at any stage of the development of the software product. The model is named a Hemant-Vipin (HV) process model and may be helpful for the software industries for development of the efficient software products and timely delivery at the end of the client. The efficiency of the HV process model is observed by considering various kinds of factors like requirement clarity, user feedback, change agility, predictability, risk identification, practical implementation, customer satisfaction, incremental development, use of ready-made components, quick design, resource organization and many more and found through a case study that the presented approach covers many of parameters in comparison of the existing process models. .

How Software Engineering Transforms Organizations: An Open and Qualitative Study on the Organizational Objectives and Motivations in Agile Transformations

Agile Transformations are challenging processes for organizations that look to extend the benefits of Agile philosophy and methods beyond software engineering.Despite the impact of these transformations on orga-nizations,they have not been extensively studied in academia.We conducted a study grounded in workshops and interviews with 99 participants from 30 organizations,including organizations undergoing transformations(“final organizations”)and companies supporting these processes(“consultants”).The study aims to understand the motivations,objectives,and factors driving and challenging these transformations.Over 700 responses were collected to the question and categorized into 32 objectives.The findings show that organizations primarily aim to achieve customer centricity and adaptability,both with 8%of the mentions.Other primary important objectives,with above 4%of mentions,include alignment of goals,lean delivery,sustainable processes,and a flatter,more team-based organizational structure.We also detect discrepancies in perspectives between the objectives identified by the two kinds of organizations and the existing agile literature and models.This misalignment highlights the need for practitioners to understand with the practical realities the organizations face.

A Hybrid Model for Improving Software Cost Estimation in Global Software Development

Accurate software cost estimation in Global Software Development(GSD)remains challenging due to reliance on historical data and expert judgments.Traditional models,such as the Constructive Cost Model(COCOMO II),rely heavily on historical and accurate data.In addition,expert judgment is required to set many input parameters,which can introduce subjectivity and variability in the estimation process.Consequently,there is a need to improve the current GSD models to mitigate reliance on historical data,subjectivity in expert judgment,inadequate consideration of GSD-based cost drivers and limited integration of modern technologies with cost overruns.This study introduces a novel hybrid model that synergizes the COCOMO II with Artificial Neural Networks(ANN)to address these challenges.The proposed hybrid model integrates additional GSD-based cost drivers identified through a systematic literature review and further vetted by industry experts.This article compares the effectiveness of the proposedmodelwith state-of-the-artmachine learning-basedmodels for software cost estimation.Evaluating the NASA 93 dataset by adopting twenty-six GSD-based cost drivers reveals that our hybrid model achieves superior accuracy,outperforming existing state-of-the-artmodels.The findings indicate the potential of combining COCOMO II,ANN,and additional GSD-based cost drivers to transform cost estimation in GSD.

普通烟草SBP基因家族生物信息学分析

SBP基因属于最大的转录因子家族之一,SBP基因家族已在许多物种中被发现和报道,但在烟草中尚未得到很好的研究。研究共在烟草中鉴定出29个SBP基因,并根据其染色体分布将其命名为NtSBP01~NtSBP29。分析了烟草SBP成员的理化性状、染色体定位、系统发育树、基因结构、保守基序和共线性分析等。根据系统发育树将NtSBP基因分为Ground1~8类。同源性分析发现,NtSBP基因中存在10对同源基因,烟草与拟南芥之间存在5对同源基因。顺式作用元件分析表明,烟草SBP基因可能参与光、低温响应和生长发育调节。研究结果为进一步的功能分析奠定了基础。

Development and Evaluation of 3D Delivery Animation Software Designed to Improve the Mother’s and Spouse’s Satisfaction with Delivery

Purpose: To clarify the effectiveness of 3-D delivery animation software for the mother’s and husband’s satisfaction with delivery. Subjects and Method: We independently developed a software application used to display the pelvic region and explain the labor process. The study involved a collaboration with hospital staff who recruited 18 primiparous and 18 multiparous mothers who were hospitalized for delivery at Facility A. The midwife explained the process of delivery using the “Delivery Animation Software”. A self-administered, anonymous questionnaire was distributed and analyzed separately for primiparous and multiparous mothers and their husbands. Results: 1) For both primiparous and multiparous couples, both mothers and their husbands gained a significantly higher level of understanding after delivery than during pregnancy. 2) The Self-Evaluation Scale for Experience of Delivery results were as follows: “I did my best for the baby even if it was painful” was selected more often for “birth coping skills”;“reliable medical staff” was selected more often for “physiological birth process”;“the birth progressed as I expected” was selected frequently by primiparous mothers;and “the birth progressed smoothly” was selected often by multiparous mothers. 3) In terms of husbands’ satisfaction with the delivery, “I was satisfied with the delivery”, “I was given an easy-to-understand explanation”, and “They explained the process to me” was selected of primiparous and multiparous fathers. 4) All primiparous and multiparous mothers positively evaluated whether the delivery animation was helpful in understanding the process of delivery. Conclusion: The delivery animation was effective in improving the understanding and satisfaction of both the mothers and their husbands.

A Proposed Approach for Measuring Maturity Level of Software Delivery

Software delivery is vital for modern organizations, driving innovation and competitiveness. Measuring an organization’s maturity in software delivery is crucial for efficiency and quality. The Capability Maturity Model (CMM) framework provides a roadmap for improvement but assessing an organization’s CMM Level is challenging. This paper offers a quantitative approach tailored to the CMM framework, using Goal-Question-Metric (GQM) frame-works for each key process area (KPA). These frameworks include metrics and questions to compute maturity scores effectively. The study also refines practices into questions for a thorough assessment. The result is an Analysis Matrix that calculates weighted scores and an overall maturity score. This approach helps organizations assess and enhance their software delivery processes systematically, aiming for improved practices and growth.

A Tutorial on Federated Learning from Theory to Practice:Foundations,Software Frameworks,Exemplary Use Cases,and Selected Trends

When data privacy is imposed as a necessity,Federated learning(FL)emerges as a relevant artificial intelligence field for developing machine learning(ML)models in a distributed and decentralized environment.FL allows ML models to be trained on local devices without any need for centralized data transfer,thereby reducing both the exposure of sensitive data and the possibility of data interception by malicious third parties.This paradigm has gained momentum in the last few years,spurred by the plethora of real-world applications that have leveraged its ability to improve the efficiency of distributed learning and to accommodate numerous participants with their data sources.By virtue of FL,models can be learned from all such distributed data sources while preserving data privacy.The aim of this paper is to provide a practical tutorial on FL,including a short methodology and a systematic analysis of existing software frameworks.Furthermore,our tutorial provides exemplary cases of study from three complementary perspectives:i)Foundations of FL,describing the main components of FL,from key elements to FL categories;ii)Implementation guidelines and exemplary cases of study,by systematically examining the functionalities provided by existing software frameworks for FL deployment,devising a methodology to design a FL scenario,and providing exemplary cases of study with source code for different ML approaches;and iii)Trends,shortly reviewing a non-exhaustive list of research directions that are under active investigation in the current FL landscape.The ultimate purpose of this work is to establish itself as a referential work for researchers,developers,and data scientists willing to explore the capabilities of FL in practical applications.

Software Vulnerability Mining and Analysis Based on Deep Learning

In recent years,the rapid development of computer software has led to numerous security problems,particularly software vulnerabilities.These flaws can cause significant harm to users’privacy and property.Current security defect detection technology relies on manual or professional reasoning,leading to missed detection and high false detection rates.Artificial intelligence technology has led to the development of neural network models based on machine learning or deep learning to intelligently mine holes,reducing missed alarms and false alarms.So,this project aims to study Java source code defect detection methods for defects like null pointer reference exception,XSS(Transform),and Structured Query Language(SQL)injection.Also,the project uses open-source Javalang to translate the Java source code,conducts a deep search on the AST to obtain the empty syntax feature library,and converts the Java source code into a dependency graph.The feature vector is then used as the learning target for the neural network.Four types of Convolutional Neural Networks(CNN),Long Short-Term Memory(LSTM),Bi-directional Long Short-Term Memory(BiLSTM),and Attention Mechanism+Bidirectional LSTM,are used to investigate various code defects,including blank pointer reference exception,XSS,and SQL injection defects.Experimental results show that the attention mechanism in two-dimensional BLSTM is the most effective for object recognition,verifying the correctness of the method.

完全匹配层在矩阵式波动方程SBP-SAT方法应用

数值离散方法和截断边界效果是地震动模拟实现的关键。基于分部求和(summation-by-parts,SBP)和一致逼近(simultaneous approximation term,SAT)的SBP-SAT方法具有较高的稳定性,这使得该方法具备了较高的应用前景和价值。此外,完全匹配层(perfect matching layer,PML)是一种应用广泛用于模拟截断边界的技术,但引入匹配层可能会破坏原始方程的稳定性,特别是在各向异性介质或曲线域模型中。首先基于数理推导,给出弹性波动方程系数矩阵的对称形式。在此基础上,引入多轴完全匹配层(multi-axis perfect matching layer,MPML),并建立相应的匹配层方程。通过本征值分析,我们可以判断阻尼函数对原方程特征根实部的走向和取值范围的影响。然后,我们采用SBP-SAT方法对矩阵对称形式匹配层方程进行离散,并在频域中采用能量法进行稳定性评估。通过对不同模型的数值仿真,表明所提出的离散框架具有整合度高、稳定性好和拓展性强等特点。此外,多轴匹配层可以与SBP-SAT方法结合,可以稳定地模拟曲线域中的波传播。

SCIRD: Revealing Infection of Malicious Software in Edge Computing-Enabled IoT Networks

The Internet of Things(IoT)has characteristics such as node mobility,node heterogeneity,link heterogeneity,and topology heterogeneity.In the face of the IoT characteristics and the explosive growth of IoT nodes,which brings about large-scale data processing requirements,edge computing architecture has become an emerging network architecture to support IoT applications due to its ability to provide powerful computing capabilities and good service functions.However,the defense mechanism of Edge Computing-enabled IoT Nodes(ECIoTNs)is still weak due to their limited resources,so that they are susceptible to malicious software spread,which can compromise data confidentiality and network service availability.Facing this situation,we put forward an epidemiology-based susceptible-curb-infectious-removed-dead(SCIRD)model.Then,we analyze the dynamics of ECIoTNs with different infection levels under different initial conditions to obtain the dynamic differential equations.Additionally,we establish the presence of equilibrium states in the SCIRD model.Furthermore,we conduct an analysis of the model’s stability and examine the conditions under which malicious software will either spread or disappear within Edge Computing-enabled IoT(ECIoT)networks.Lastly,we validate the efficacy and superiority of the SCIRD model through MATLAB simulations.These research findings offer a theoretical foundation for suppressing the propagation of malicious software in ECIoT networks.The experimental results indicate that the theoretical SCIRD model has instructive significance,deeply revealing the principles of malicious software propagation in ECIoT networks.This study solves a challenging security problem of ECIoT networks by determining the malicious software propagation threshold,which lays the foundation for buildingmore secure and reliable ECIoT networks.

ProTSA: A Testing Process for Automotive Software Domain

This study evaluates the development of a testing process for the automotive software domain, highlighting challenges stemming from the absence of adequate processes. The research demonstrates the application of Design Science Research methodology in developing, an automotive software testing process—ProTSA, using six functional testing modules. Additionally, the study evaluates the benefits of implementing ProTSA in a specific Original Equipment Manufacturer (OEM) using an experimental single-case approach with industry professionals’ participation through a survey. The study concludes that combining testing techniques with effective communication and alignment is crucial for enhancing software quality. Furthermore, survey data indicates that implementing ProTSA leads to productivity gains by initiating tests early, resulting in time savings in the testing program and increased productivity for the testing team. Future work will explore implementing ProTSA in cybersecurity, over-the-air software updates, and autonomous vehicle testing processes. .

Strategic Contracting for Software Upgrade Outsourcing in Industry 4.0

The advent of Industry 4.0 has compelled businesses to adopt digital approaches that combine software toenhance production efficiency. In this rapidly evolving market, software development is an ongoing process thatmust be tailored to meet the dynamic needs of enterprises. However, internal research and development can beprohibitively expensive, driving many enterprises to outsource software development and upgrades to externalservice providers. This paper presents a software upgrade outsourcing model for enterprises and service providersthat accounts for the impact of market fluctuations on software adaptability. To mitigate the risk of adverseselection due to asymmetric information about the service provider’s cost and asymmetric information aboutthe enterprise’s revenues, we propose pay-per-time and revenue-sharing contracts in two distinct informationasymmetry scenarios. These two contracts specify the time and transfer payments for software upgrades. Througha comparative analysis of the optimal solutions under the two contracts and centralized decision-making withfull-information, we examine the characteristics of the solutions under two information asymmetry scenarios andanalyze the incentive effects of the two contracts on the various stakeholders. Overall, our study offers valuableinsights for firms seeking to optimize their outsourcing strategies and maximize their returns on investment insoftware upgrades.