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.

Strategy and Methodology of Integration Testing for GUI Software

In this paper, by means of effective testing practices, main strategies of integration testing for GUI software, including differentiating strategy for distinguished system, strategy of personnel organization, incremental testing strategy based on baseline version, testing strategy of circulating loop through the whole life, and the strategy of test suite construction, were briefly investigated. Moreover, for the code analysis, the FTA (Fault Tree analysis) is proposed to deal with the software change in regression testing. For test suite constructing, the constructing methods for baseline version and the incremental change are deeply discussed, in which main points focus on the testing strategy based on “Sheet/Form”, the “Grey-box approach” for integration testing process, and the application of the improved STD (State Transform Diagram) in state testing. At the same time, the suite construction of integration testing for two types, including small scale program and large scale software, is analyzed and discussed in detail. For testing execution, the specific method based on “Cross-testing” is investigated. Concurrently, by a lot of examples, all results of testing activity indicate that these strategies and methods are useful and fitted to integration testing for GUI software.

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.

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 systematic scoping review of study methodology for randomized controlled trials investigating probiotics in athletic and physically active populations

Background:The purported ergogenic and health effects of probiotics have been a topic of great intrigue among researchers,practitioners,and the lay public alike.There has also been an increased research focus within the realm of sports science and exercise medicine on the athletic gut microbiota.However,compared to other ergogenic aids and dietary supplements,probiotics present unique study challenges.The objectives of this systematic scoping review were to identify and characterize study methodologies of randomized controlled trials investigating supplementation with probiotics in athletes and physically active individuals.Methods:Four databases(MEDLINE,CINAHL,Cochrane CENTRAL,and Cochrane Database of Systematic Reviews)were searched for randomized controlled studies involving healthy athletes or physically active individuals.An intervention with probiotics and inclusion of a control and/or placebo group were essential.Only peer-reviewed articles in English were considered,and there were no date restrictions.Results were extracted and presented in tabular form to detail study protocols,characteristics,and outcomes.Bias in randomized controlled trials was determined with the RoB 2.0 tool.Results:A total of 45 studies were included in the review,with 35 using a parallel group design and 10 using a cross-over design.Approximately half the studies used a single probiotic and the other half a multi-strain preparation.The probiotic dose ranged from 2×10^(8)to 1×10^(11)colony forming units daily,and the length of intervention was between 7 and 150 days.Fewer than half the studies directly assessed gastrointestinal symptoms,gut permeability,or the gut microbiota.The sex ratio of participants was heavily weighted toward males,and only 3 studies exclusively investigated females.Low-level adverse events were reported in only 2 studies,although the methodology of reporting varied widely.The risk of bias was generally low,although details on randomization were lacking in some studies.Conclusion:There is a substantial body of research on the effects of prob iotic supplementation in healthy athletes and physically active individuals.Considerable heterogeneity in probiotic selection and dosage as well as outcome measures has made clinical and mechanistic interpretation challenging for both health care practitioners and researchers.Attention to issues of randomization of participants,treatments and interventions,selection of outcomes,demographics,and reporting of adverse events will facilitate more trustworthy interpretation of probiotic study results and inform evidence-based guidelines.

Enhancing wood efficiency through comprehensive wood flow analysis:Methodology and strategic insights

Wood,an essential natural resource in human civilization,remains widely used despite advances in technology and material substitution.The surge in greenhouse gas emissions and environmental concerns accentuates the need for optimizing wood utilization.Material flow analysis is a powerful tool for tracking material flows and stocks,aiding resource management and environmental decision-making.However,the full extent of its methodological dimensions,particularly within the context of the wood supply chain,remains relatively unexplored.In this study,we delve into the existing literature on wood flow analysis,discussing its primary objectives,materials involved,temporal and spatial scales,data sources,units,and conversion factors.Additionally,data uncertainty,data reconciliation and crucial assumptions in material flow analysis are highlighted in this paper.Key findings reveal the significance of wood cascading and substitution effects by replacing non-wood materials,where they can reduce greenhouse gas emissions more than the natural carbon sink of forests and wood products.The immediate impact of short-term wood cascading might not be as robust as the substitution effect,with energy substitution showcasing better results than material substitution.However,it's crucial to note that these conclusions could experience significant reversal from a long-term and global perspective.Strategies for improving wood efficiency involve maximizing material use,advancing construction technologies,extending product lifespans,promoting cascade use,and optimizing energy recovery processes.The study underscores the need for standardized approaches in wood flow analysis and emphasizes the potential of wood efficiency strategies in addressing environmental challenges.

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.

Optimization and Characterization of Cellulose Extraction from Grevillea robusta (Silky Oak) Leaves by Soda-Anthraquinone Pulping Using Response Surface Methodology

Response surface methodology (RSM) using the central composite design (CCD) was applied to examine the impact of soda-anthraquinone pulping conditions on Grevillea robusta fall leaves. The pulping factors studied were: NaOH charge 5% to 20% w/v, pulping time 30 to 180 minutes, and the anthraquinone charge 0.1 to 0.5% w/w based on the oven-dried leaves. The responses evaluated were the pulp yield, cellulose content, and the degree of delignification. Various regression models were used to evaluate the effects of varying the pulping conditions. The optimum conditions attained were;NaOH charge of 14.63%, 0.1% anthraquinone, and a pulping period of 154 minutes, corresponding to 20.68% pulp yield, 80.56% cellulose content, and 70.34% lignin removal. Analysis of variance (ANOVA), was used to determine the most important variables that improve the extraction process of cellulose. The experiment outcomes matched those predicted by the model (Predicted R2 = 0.9980, Adjusted R2 = 0.9994), demonstrating the adequacy of the model used. FTIR analysis confirmed the elimination of the non-cellulosic fiber constituents. The lignin and hemicellulose-related bands (around 1514 cm−1, 1604 cm−1, 1239 cm−1, and 1734 cm−1) decreased with chemical treatment, indicating effective cellulose extraction by the soda-anthraquinone method. Similar results were obtained by XRD, SEM and thermogravimetric analysis of the extracted cellulose. Therefore, Grevillea robusta fall leaves are suitable renewable, cost-effective, and environmentally friendly non-wood biomass for cellulose extraction.

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.

Research on the development methodology for clinical practice guidelines for organic integration of traditional Chinese medicine and Western medicine

Integrated traditional Chinese medicine(TCM)and Western medicine(WM)is a new medical science grounded in the knowledge bases of both TCM and WM,which then forms a unique modern medical system in China.Integrated TCM and WM has a long history in China,and has made important achievements in the process of clinical diagnosis and treatment.However,the methodological defects in currently published clinical practice guidelines(CPGs)limit its development.The organic integration of TCM and WM is a deeper integration of TCM and WM.To realize the progression of"integration"to"organic integration",a targeted and standardized guideline development methodology is needed.Therefore,the purpose of this study is to establish a standardized development procedure for clinical practice guidelines for the organic integration of TCM and WM to promote the systematic integration of TCM and WM research results into clinical practice guidelines in order to achieve optimal results as the whole is greater than the sum of the parts.

The Influence of Tartaric Acid in the Silver Nanoparticle Synthesis Using Response Surface Methodology

Silver nanoparticles(AgNPs)synthesized using tartaric acid as a capping agent have a great impact on the reaction kinetics and contribute significantly to the stability of AgNPs.The protective layer formed by tartaric acid is an important factor that protects the silver surface and reduces potential cytotoxicity problems.These attributes are critical for assessing the compatibility of AgNPs with biological systems and making them suitable for drug delivery applications.The aim of this research is to conduct a comprehensive study of the effect of tartaric acid concentration,sonication time and temperature on the formation of silver nanoparticles.Using Response Surface Methodology(RSM)with Face-Centered Central Composite Design(FCCD),the optimization process identifies the most favorable synthesis conditions.UV-Vis spectrum regression analysis shows that AgNPs stabilized with tartaric acid are more stable than AgNPs without tartaric acid.This highlights the increased stability that tartaric acid provides in AgNP ssssynthesis.Particle size distribution analysis showed a multimodal distribution for AgNPs with tartaric acid and showed the smallest size peak with an average size of 20.53 nm.The second peak with increasing intensity shows a dominant average size of 108.8 nm accompanied by one standard deviation of 4.225 nm and a zeta potential of−11.08 mV.In contrast,AgNPs synthesized with polyvinylpyrrolidone(PVP)showed a unimodal particle distribution with an average particle size of 81.62 nm and a zeta potential of−2.96 mV.The more negative zeta potential of AgNP-tartaric acid indicates its increased stability.Evaluation of antibacterial activity showed that AgNPs stabilized with tartaric acid showed better performance against E.coli and B.subtilis bacteria compared with AgNPs-PVP.In summary,this study highlights the potential of tartaric acid in AgNP synthesis and suggests an avenue for the development of stable AgNPs with versatile applications.

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. .

How to use the Surveillance,Epidemiology,and End Results(SEER)data:research design and methodology

In the United States(US),the Surveillance,Epidemiology,and End Results(SEER)program is the only comprehensive source of population-based information that includes stage of cancer at the time of diagnosis and patient survival data.This program aims to provide a database about cancer incidence and survival for studies of surveillance and the development of analytical and methodological tools in the cancer field.Currently,the SEER program covers approximately half of the total cancer patients in the US.A growing number of clinical studies have applied the SEER database in various aspects.However,the intrinsic features of the SEER database,such as the huge data volume and complexity of data types,have hindered its application.In this review,we provided a systematic overview of the commonly used methodologies and study designs for retrospective epidemiological research in order to illustrate the application of the SEER database.Therefore,the goal of this review is to assist researchers in the selection of appropriate methods and study designs for enhancing the robustness and reliability of clinical studies by mining the SEER database.