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.

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.

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. .

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.

Agile Development Methods in Software Engineering and Their Efficiency Analysis

This paper delves into Agile Development Methods in Software Engineering,contrasting them with the traditional Waterfall model and analyzing their efficiency.Agile methods,known for their adaptability and customer-centric approach,have gained prominence in the fast-paced software development industry.These methods,including Scrum,Kanban,and Extreme Programming(XP),are characterized by iterative cycles,collaborative efforts,and a focus on rapid delivery and quality improvement.The paper compares these agile methodologies to the sequential and rigid Waterfall model,highlighting agile’s superior flexibility,adaptability,and responsiveness to changing requirements.It emphasizes the importance of customer involvement in agile processes,which leads to higher satisfaction and better alignment with user expectations.The analysis reveals that agile methods not only enhance the speed of delivery but also improve the overall quality of the software product.The paper concludes that agile methodologies are more effective in today's dynamic software development environment,providing a robust framework for managing complex projects and ensuring the delivery of high-quality,relevant software solutions.

Development and technology status of energy storage in depleted gas reservoirs

Utilizing energy storage in depleted oil and gas reservoirs can improve productivity while reducing power costs and is one of the best ways to achieve synergistic development of"Carbon Peak–Carbon Neutral"and"Underground Resource Utiliza-tion".Starting from the development of Compressed Air Energy Storage(CAES)technology,the site selection of CAES in depleted gas and oil reservoirs,the evolution mechanism of reservoir dynamic sealing,and the high-flow CAES and injection technology are summarized.It focuses on analyzing the characteristics,key equipment,reservoir construction,application scenarios and cost analysis of CAES projects,and sorting out the technical key points and existing difficulties.The devel-opment trend of CAES technology is proposed,and the future development path is scrutinized to provide reference for the research of CAES projects in depleted oil and gas reservoirs.

Adaptive Sparse Grid Discontinuous Galerkin Method:Review and Software Implementation

This paper reviews the adaptive sparse grid discontinuous Galerkin(aSG-DG)method for computing high dimensional partial differential equations(PDEs)and its software implementation.The C++software package called AdaM-DG,implementing the aSG-DG method,is available on GitHub at https://github.com/JuntaoHuang/adaptive-multiresolution-DG.The package is capable of treating a large class of high dimensional linear and nonlinear PDEs.We review the essential components of the algorithm and the functionality of the software,including the multiwavelets used,assembling of bilinear operators,fast matrix-vector product for data with hierarchical structures.We further demonstrate the performance of the package by reporting the numerical error and the CPU cost for several benchmark tests,including linear transport equations,wave equations,and Hamilton-Jacobi(HJ)equations.

BMPRⅡ^(+)neural precursor cells isolated and characterized from organotypic neurospheres:an in vitro model of human fetal spinal cord development

Roof plate secretion of bone morphogenetic proteins(BMPs)directs the cellular fate of sensory neurons during spinal cord development,including the formation of the ascending sensory columns,though their biology is not well understood.Type-ⅡBMP receptor(BMPRⅡ),the cognate receptor,is expressed by neural precursor cells during embryogenesis;however,an in vitro method of enriching BMPRⅡ^(+)human neural precursor cells(hNPCs)from the fetal spinal cord is absent.Immunofluorescence was undertaken on intact second-trimester human fetal spinal cord using antibodies to BMPRⅡand leukemia inhibitory factor(LIF).Regions of highest BMPRⅡ^(+)immunofluorescence localized to sensory columns.Parenchymal and meningeal-associated BMPRⅡ^(+)vascular cells were identified in both intact fetal spinal cord and cortex by co-positivity with vascular lineage markers,CD34/CD39.LIF immunostaining identified a population of somas concentrated in dorsal and ventral horn interneurons,mirroring the expression of LIF receptor/CD118.A combination of LIF supplementation and high-density culture maintained culture growth beyond 10 passages,while synergistically increasing the proportion of neurospheres with a stratified,cytoarchitecture.These neurospheres were characterized by BMPRⅡ^(+)/MAP2ab^(+/–)/βⅢ-tubulin^(+)/nestin^(–)/vimentin^(–)/GFAP^(–)/NeuN^(–)surface hNPCs surrounding a heterogeneous core ofβⅢ-tubulin^(+)/nestin^(+)/vimentin^(+)/GFAP^(+)/MAP2ab^(–)/NeuN^(–)multipotent precursors.Dissociated cultures from tripotential neurospheres contained neuronal(βⅢ-tubulin^(+)),astrocytic(GFAP+),and oligodendrocytic(O4+)lineage cells.Fluorescence-activated cell sorting-sorted BMPRⅡ^(+)hNPCs were MAP2ab^(+/–)/βⅢ-tubulin^(+)/GFAP^(–)/O4^(–)in culture.This is the first isolation of BMPRⅡ^(+)hNPCs identified and characterized in human fetal spinal cords.Our data show that LIF combines synergistically with high-density reaggregate cultures to support the organotypic reorganization of neurospheres,characterized by surface BMPRⅡ^(+)hNPCs.Our study has provided a new methodology for an in vitro model capable of amplifying human fetal spinal cord cell numbers for>10 passages.Investigations of the role BMPRⅡplays in spinal cord development have primarily relied upon mouse and rat models,with interpolations to human development being derived through inference.Because of significant species differences between murine biology and human,including anatomical dissimilarities in central nervous system(CNS)structure,the findings made in murine models cannot be presumed to apply to human spinal cord development.For these reasons,our human in vitro model offers a novel tool to better understand neurodevelopmental pathways,including BMP signaling,as well as spinal cord injury research and testing drug therapies.

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.

Constructing a standard system of city sustainable development assessment technology based on the SDGs

Given the rapid development of China’s new urbanization,cities with different locations and varying functional positioning,resource endowments,and development stages have insufficient scientific and applicable technical tools for implementing the United Nations Sustainable Development Goals(SDGs).City managers and policymakers must urgently establish SDG benchmarks to diagnose city development.Moreover,successful experiences from similar cities regarding sustainable development and self-improvement must be learned from to promote diversified,sustainable development across the country.Furthermore,emerging technologies such as artificial intelligence,the Internet of Things,big data and 5G are widely used in smart cities.Therefore,there is a growing need for“knowledge-based,personalized and intelligent”technologies to support monitoring,evaluation,and decision-making processes facilitating sustainable development in cities.This paper uses standardization as the theoretical support and technical basis.This approach can help clarify the sustainable development processes in China and clarify the evaluation results of and provide data on horizontal city comparisons,which can be used to develop evaluation technology for sustainable development in cities and construct a standardized system.The results provide a standard framework for intelligent assessment and decision-making regarding cities’sustainable development capabilities in China.Evaluating major international standardization institutions reveals that the practices of Chinese national standards should be fully absorbed and integrated to guide the evaluation of smart,resilient,and low-carbon cities.To this end,an indicator library of city sustainable development is proposed to provide standard evaluation technology methods.Finally,analyzing the response relationship of the indicator library to SDGs reveals the need for a standardized knowledge map of sustainable development assessment techniques and methods from the perspective of integrated management for sustainable development in cities.

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.

Spatial Differentiation and Convergence Trend of High-quality Development Level of China’s Tourism Economy

This paper aims to interpret the connotation of high-quality development of tourism economy(HQTE)from the perspective of the new development concepts of innovation,coordination,green,openness and sharing,and then to evaluate the spatial differenti-ation of China’s HQTE based on provincial panel data from 2009 to 2018.Specifically,we employ the spatial convergence model to ex-plore the absolute and conditionalβconvergence trends of HQTE in the whole country and the eastern,central and western regions of China.Our empirical results reveal that:1)within the decade,from 2009 to 2018,regions of China with the highest HQTE index is its eastern region followed by the central region and then the western region,but the fastest growing one is the western region of China fol-lowed by the central region and then the eastern region.2)Whether or not the spatial effect is included,there are absolute and condition-alβconvergence in HQTE in the whole country and aforementioned three regions.3)The degree of government attention as well as the level of economic development and location accessibility are the positive driving factors for the convergence of HQTE in the whole country and the three regions.The degree of marketization and human capital have not passed the significance test either in the whole country or in the three regions.The above conclusions could deepen the understanding of the regional imbalance and spatial conver-gence characteristics of HQTE,clarify the primary development objects,and accomplish the goal of China’s HQTE.

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.

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.

Embryonic and Larval Development of Reciprocal Crosses between Clarias gariepinus (Burchell, 1822) and Clarias jaensis (Boulenger, 1909) in West Cameroon

Establishing intraspecific breeding and hybridization programs and determining genetic variability are two important issues for aquaculture. However, interspecific hybridization to improve growth and feeding efficiency is limited. For this purpose, the embryonic and larval development of reciprocal crosses of Clarias gariepinus (Burchell, 1822) and Clarias jaensis (Boulenger, 1909) were studied under laboratory conditions. The fertilization rate varied from 63.33% to 92%, while the hatching rate ranged from 55.68% to 76% with the highest value in hybrids ♀Cg × ♂Cj. Crosses between ♀Cj × ♂Cj, ♀Cg × ♂Cj and ♀Cj × ♂Cg had embryonic stages similar to those of the pure sib ♀Cg x ♂Cg. All crosses, however, had different timing for the various embryological stages. Hatching occurred at 32 h 15 min and 38 h for ♀Cj × ♂Cj and ♀Cj × ♂Cg, and 23 h and 23 h 30 min, respectively, for ♀Cg × ♂Cg and ♀Cg × ♂Cj. However, both crosses produced viable larvae until the first external feeding. The external morphological features of the larvae were completely formed by the 10th day after hatching. The body forms of the crosses at this time were indistinguishable from the pure sib. This study thus laid the groundwork for further comparative studies on hybrid performance and characterization.

A Defect Detection Method for the Primary Stage of Software Development

In the early stage of software development,a software requirements specification(SRS)is essential,and whether the requirements are clear and explicit is the key.However,due to various reasons,there may be a large number of misunderstandings.To generate high-quality software requirements specifications,numerous researchers have developed a variety of ways to improve the quality of SRS.In this paper,we propose a questions extraction method based on SRS elements decomposition,which evaluates the quality of SRS in the form of numerical indicators.The proposed method not only evaluates the quality of SRSs but also helps in the detection of defects,especially the description problem and omission defects in SRSs.To verify the effectiveness of the proposed method,we conducted a controlled experiment to compare the ability of checklist-based review(CBR)and the proposed method in the SRS review.The CBR is a classicmethod of reviewing SRS defects.After a lot of practice and improvement for a long time,CBR has excellent review ability in improving the quality of software requirements specifications.The experimental results with 40 graduate studentsmajoring in software engineering confirmed the effectiveness and advantages of the proposed method.However,the shortcomings and deficiencies of the proposed method are also observed through the experiment.Furthermore,the proposed method has been tried out by engineers with practical work experience in software development industry and received good feedback.

Development of a software platform for bridge modal and damage identification based on ambient excitation

Modal and damage identification based on ambient excitation can greatly improve the efficiency of high-speed railway bridge vibration detection.This paper first describes the basic principles of stochastic subspace identification,peak-picking,and frequency domain decomposition method in modal analysis based on ambient excitation,and the effectiveness of these three methods is verified through finite element calculation and numerical simulation,Then the damage element is added to the finite element model to simulate the crack,and the curvature mode difference and the curvature mode area difference square ratio are calculated by using the stochastic subspace identification results to verify their ability of damage identification and location.Finally,the above modal and damage identification techniques are integrated to develop a bridge modal and damage identification software platform.The final results show that all three modal identification methods can accurately identify the vibration frequency and mode shape,both damage identification methods can accurately identify and locate the damage,and the developed software platform is simple and efficient.

Development and formation of wing cuticle based on transcriptomic analysis in Locusta migratoria during metamorphosis

Wings are an important flight organ of insects.Wing development is a complex process controlled by a series of genes.The flightless wing pad transforms into a mature wing with the function of migratory flight during the nymphto-adult metamorphosis.However,the mechanism of wing morphogenesis in locusts is still unclear.This study analyzed the microstructures of the locust wing pads at pre-eclosion and the wings after eclosion and performed the comparative transcriptome analysis.RNA-seq identified 25,334 unigenesand 3,430 differentially expressed genes(DEGs)(1,907 up-regulated and 1,523 down-regulated).The DEGs mainly included cuticle development(LmACPs),chitin metabolism(Lm Idgf4),lipid metabolism-related genes,cell adhesion(Integrin),zinc finger transcription factors(LmSalm,LmZF593 andLmZF521),and others.Functional analysis based on RNA interference and hematoxylin and eosin(H&E)staining showed that the three genes encoded zinc finger transcription factors are essential for forming wing cuticle and maintaining morphology in Locusta migratoria.Finally,the study found that the LmSalm regulates the expression of LmACPs in the wing pads at pre-eclosion,and LmZF593 and LmZF521 regulate the expression of LmIntegrin/LmIdgf4/LmHMT420 in the wings after eclosion.This study revealed that the molecular regulatory axis controls wing morphology in nymphal and adult stages of locusts,offering a theoretical basis for the study of wing development mechanisms in hemimetabolous insects.