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Managing Safety and Resilience Dangers Throughout the Lifecycle

Software Engineering

codesanitize

-

6 August 2025

Software program is a rising element of right now’s mission-critical techniques. As organizations turn out to be extra depending on software-driven expertise, safety and resilience dangers to their missions additionally enhance. Managing these dangers is simply too typically deferred till after deployment on account of competing priorities, reminiscent of satisfying value and schedule targets. Nonetheless, failure to deal with these dangers early within the techniques lifecycle can’t solely enhance operational impression and mitigation prices, however it might additionally severely restrict administration choices.

For Division of Protection (DoD) weapon techniques, it’s particularly necessary to handle software program safety and resilience dangers. Proactively figuring out and correcting software program vulnerabilities and weaknesses minimizes the danger of cyber-attacks, weapons system failures, and different disruptions that might jeopardize DoD missions. The GAO has recognized software program and cybersecurity as persistent challenges throughout the portfolio of DoD weapon techniques. To deal with these challenges, acquisition packages ought to begin managing a system’s safety and resilience dangers early within the lifecycle and proceed all through the system’s lifespan.

This submit introduces the Safety Engineering Framework, an in depth schema of software-focused engineering practices that acquisition packages can use to handle safety and resilience dangers throughout the lifecycle of software-reliant techniques.

Software program Assurance

Software program assurance is a stage of confidence that, all through its lifecycle, software program capabilities as meant and is freed from vulnerabilities, both deliberately or unintentionally designed or inserted as a part of the software program. Software program assurance is more and more necessary to organizations throughout all sectors due to software program’s rising affect in mission-critical techniques. Managing software program assurance is a problem due to the expansion in functionality, complexity, and interconnection amongst software-reliant techniques.

For instance, think about how the scale of flight software program has elevated over time. Between 1960 and 2000, the extent of general system performance that software program supplies to navy plane pilots elevated from 8 % to 80 %. On the identical time, the scale of software program in navy plane grew from 1,000 strains of code within the F-4A to 1.7 million strains of code (MLOC) within the F-22 and 8 million strains within the F-35. This development is predicted to proceed over time. As software program exerts extra management over complicated techniques (e.g., navy plane), the potential danger posed by vulnerabilities will enhance correspondingly.

Software program Defects and Vulnerabilities: A Lifecyle Perspective

Determine 1 beneath highlights the speed of defect introduction and identification throughout the lifecycle. This was derived from knowledge introduced within the SEI report Reliability Validation and Enchancment Framework. Research of safety-critical techniques, notably DoD avionics software program techniques, present that 70 % of all errors are launched throughout necessities and structure design actions. Nonetheless, solely 20 % of the errors are discovered by the top of code growth and unit check, whereas 80.5 % of the errors are found at or after integration testing. The rework effort to appropriate necessities and design issues in later phases will be as excessive as 300 to 1,000 instances the trouble of in-phase correction. Even after the rework, undiscovered errors are prone to stay.

Determine 1: Fee of Defect Introduction and Identification throughout the Lifecycle

Given the complexities concerned in creating large-scale, software-reliant techniques, it’s comprehensible that no software program is freed from dangers. Defects exist even within the highest high quality software program. For instance, best-in-class code can have as much as 600 defects per MLOC, whereas average-quality code usually has round 6,000 defects per MLOC, and a few of these defects are weaknesses that may result in vulnerabilities. Analysis signifies that roughly 5 % of software program defects are safety vulnerabilities. Because of this, best-in-class code can have as much as 30 vulnerabilities per MLOC. For average-quality code, the variety of safety vulnerabilities will be as excessive as 300 per MLOC. You will need to notice that the defect charges cited listed here are estimates that present basic perception into the problem of code high quality and variety of vulnerabilities in code. Defect charges in particular initiatives can range enormously. Nonetheless, these estimates spotlight the significance of decreasing safety vulnerabilities in code throughout software program growth. Safe coding practices, code evaluations, and code evaluation instruments are necessary methods to determine and proper recognized weaknesses and vulnerabilities in code.

As illustrated in Determine 1, safety and resilience should be managed throughout the lifecycle, beginning with the event of high-level system necessities by means of operations and sustainment (O&S). Program and system stakeholders ought to apply main practices for buying, engineering, and deploying safe and resilient software-reliant techniques. In 2014, the SEI initiated an effort to doc main practices for managing safety and resilience dangers throughout the techniques lifecycle, offering an strategy for constructing safety and resilience right into a system slightly than bolting them on after deployment. This effort produced a number of cybersecurity engineering options, most notably the Safety Engineering Threat Evaluation (SERA) methodology and the Acquisition Safety Framework (ASF). Late final yr, the SEI launched the Safety Engineering Framework.

Safety Engineering Framework (SEF)

The SEF is a set of software-focused engineering practices for managing safety and resilience dangers throughout the techniques lifecycle, beginning with necessities definition and persevering with by means of O&S. It supplies a roadmap for constructing safety and resilience into software-reliant techniques previous to deployment and sustaining these capabilities throughout O&S. The SEF builds on the foundational analysis of SERA and the ASF, offering in-depth steering that elaborates on main engineering practices and methods to carry out them.

SEF practices assist be sure that engineering processes, software program, and instruments are safe and resilient, thereby decreasing the danger that attackers will disrupt program and system data and belongings. Acquisition packages can use the SEF to evaluate their present engineering practices and chart a course for enchancment, in the end decreasing safety and resilience dangers in deployed software-reliant techniques.

Safety and Resilience

At its core, the SEF is a risk-based framework that addresses each safety and resilience:

Threat administration supplies the inspiration for managing safety and resilience. In actual fact, danger administration strategies, instruments, and strategies are used to handle each. Nonetheless, safety and resilience view danger from totally different views: Safety considers dangers from a safety viewpoint, whereas resilience considers danger from a perspective of adapting to situations, stresses, assaults, and compromises. As proven in Determine 2, there’s some overlap between the danger views of safety and resilience. On the identical time, safety and resilience every have distinctive dangers and mitigations.

Determine 2: Threat Views: Safety Versus Resilience

The SEF specifies practices for managing safety and resilience dangers. The attitude the group adopts—safety, resilience, or a mix of the 2—influences the dangers an acquisition group considers throughout an evaluation and the set of controls which can be accessible for danger mitigation. Due to the associated nature of safety and resilience, the SEF (and the rest of this weblog submit) makes use of the time period safety/resilience all through.

SEF Construction

As illustrated in Determine 3, the SEF has a hierarchy of domains, objectives, and practices:

Domains occupy the highest stage of the SEF hierarchy. A website captures a singular administration or technical perspective of managing safety/resilience dangers throughout the techniques lifecycle. Every area is supported by two or extra objectives, which type the following stage of the SEF hierarchy.
Targets outline the capabilities {that a} program leverages to construct safety/resilience right into a software-reliant system. Associated objectives belong to the identical SEF area.
Practices inhabit the ultimate and most detailed stage within the hierarchy. Practices describe actions that assist the achievement of SEF objectives. The SEF phrases practices as questions. Associated practices belong to the identical SEF purpose.

Determine 3: SEF Group and Construction

The SEF contains 3 domains, 13 objectives, and 119 practices. The following part describes the SEF’s domains and objectives.

Area 1: Engineering Administration

This area supplies a basis for fulfillment by making certain that safety/resilience actions are deliberate and managed. The target of Area 1 is to handle safety/resilience dangers successfully within the system being acquired and developed.

Program and engineering managers mix their technical experience with their enterprise and mission information to supply technical administration and organizational management for engineering initiatives. Managers are tasked with planning, organizing, and directing an acquisition program’s engineering and growth actions. Engineering administration is a specialised sort of administration that’s wanted to guide engineering or technical personnel and initiatives efficiently. Area 1 contains the next three objectives:

Objective 1.1: Engineering Exercise Administration. Safety/resilience engineering actions throughout the lifecycle are deliberate and managed.
Objective 1.2: Engineering Threat Administration. Safety/resilience dangers that may have an effect on the system are assessed and managed throughout system design and growth.
Objective 1.3: Unbiased Evaluation. An unbiased evaluation of this system or system is carried out.

Area 2: Engineering Actions

This area addresses the day-to-day practices which can be important for constructing safety/resilience right into a software-reliant system. The target of Area 2 is to combine safety/resilience into this system’s present engineering practices. All techniques lifecycles tackle a standard set of engineering actions, starting with necessities specification and persevering with by means of system O&S. Area 2 expands the main focus of a program’s techniques lifecycle mannequin to incorporate safety/resilience. Area 2 contains the next eight objectives:

Objective 2.1: Necessities. Safety/resilience necessities for the system and its software program elements are specified, analyzed, and managed.
Objective 2.2: Structure. Safety/resilience dangers ensuing from the system and software program architectures are assessed and mitigated.
Objective 2.3: Third-Occasion Parts. Safety/resilience dangers that may have an effect on third-party elements are recognized and mitigated.
Objective 2.4: Implementation. Safety/resilience controls are applied, and weaknesses and vulnerabilities in software program code are assessed and managed.
Objective 2.5: Take a look at and Analysis. Safety/resilience dangers that may have an effect on the built-in system are recognized and remediated throughout check and analysis.
Objective 2.6: Authorization to Function. The operation of the system is permitted, and the residual danger to operations is explicitly accepted.
Objective 2.7: Deployment. Safety/resilience is addressed in transition and deployment actions.
Objective 2.8: Operations and Sustainment. Safety/resilience dangers and points are recognized and resolved because the system is used and supported within the operational atmosphere.

Area 3: Engineering Infrastructure

This area manages safety/resilience dangers within the engineering, growth, check, and coaching environments. The targets of Area 3 are to make use of software program, instruments, and applied sciences that assist this system’s engineering and growth actions and to handle safety/resilience dangers within the engineering infrastructure. Engineers and builders use a wide range of software program, instruments, and applied sciences to assist their design and growth actions. Safety/resilience engineering software program, instruments, and applied sciences should be procured, put in, and built-in with this system’s present engineering infrastructure.

The engineering infrastructure is the a part of the IT infrastructure that helps engineering and growth actions carried out by personnel from the acquisition program, contractors, and suppliers. Because of this, the engineering infrastructure will be an assault vector into the software-reliant system that’s being acquired and developed. IT assist groups want to make sure that they’re making use of safety/resilience practices when managing the engineering infrastructure to make sure that danger is being managed appropriately. Area 3 contains the next two objectives:

Objective 3.1: Engineering Software program, Instruments, and Applied sciences. Safety/resilience engineering software program, instruments, and applied sciences are built-in with the engineering infrastructure.
Objective 3.2: Infrastructure Operations and Sustainment. Safety/resilience dangers within the engineering infrastructure are recognized and mitigated.

SEF Practices and Steering

SEF domains present the organizing construction for the framework’s technical content material, which is the gathering of objectives and practices. The SEF’s in-depth steering for all objectives and practices describes the aptitude represented by every purpose, together with its function, related context, and supporting practices. SEF steering additionally defines the important thing ideas and background data wanted to know the intent of every observe.

Safety Engineering Framework (SEF): Managing Safety and Resilience Dangers Throughout the Methods Lifecycle accommodates in-depth steering for all objectives and practices.

Companion with the SEI to Handle Safety and Resilience Dangers

The SEF paperwork main engineering practices for managing safety/resilience dangers throughout the techniques lifecycle. The SEI supplies open entry to SEF steering, strategies, and supplies. Future work associated to the SEF will focus totally on transitioning SEF ideas and practices to the neighborhood. The SEI plans to work with DoD packages to pilot the SEF and incorporate classes realized into future model of the framework.

Lastly, the SEF growth crew continues to hunt suggestions on the framework, together with how it’s getting used and utilized. This data will assist affect the long run course of the SEF in addition to the SEI’s work on documenting main practices for software program safety.

Tips on how to dynamically set views utilizing JSON in Swift iOS?

iOS Development

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6 August 2025

0

I am engaged on an iOS app that should load and show views dynamically from a distant JSON file (principal.json). This JSON accommodates coordinates (x, y, w, h) and picture names.

I’ve the code beneath, which is used to set views dynamically, however I don’t know methods to set these views first in Storyboard after which assign them dynamically utilizing the code beneath:

{
            let templateDataUrl = URL(string: url!.absoluteString + "/" + "principal.json")!
            
            let activity = URLSession.shared.dataTask(with: templateDataUrl) { information, response, error in
                guard let information = information, error == nil else {
                    print("Error loading template information: (error?.localizedDescription ?? "Unknown error")")
                    errorType().errorStatusBar()
                    return
                }
                do {
                    let templateData = attempt JSONDecoder().decode(TemplateData.self, from: information)
                    
                    DispatchQueue.principal.async { [self] in
                        
                        screenwidth = bounds.measurement.width
                        screenheight = bounds.measurement.peak
                        
                        let frameUrl = URL.init(string: url!.absoluteString + "/" + "body.png" )
                        
                        if let frameUrl = frameUrl {
                            
                            SDWebImageManager.shared.loadImage(with: frameUrl, choices: .highPriority, progress: nil, accomplished: { picture, information, error, cacheType, bool, url in
                                
                                if let error = error {
                                    print("Error loading body picture: (error.localizedDescription)")
                                    errorType().errorStatusBar()
                                } else {
                                    
                                    self.subImage.picture = picture
                                }
                            })
                        }
                        
                        let picture = UIImage(named: "body")
                        let imageSize = picture!.measurement
                        
                        let width = (imageSize.width * screenwidth) / imageSize.peak
                        self.scrollViewTemplate.contentSize = CGSize(width: width, peak: screenwidth)
                        self.scrollSubView.body = CGRect(origin: .zero, measurement: self.scrollViewTemplate.contentSize)
                        
                        for information in templateData.outcome
                        {
                            let xx = Double(information.x) ?? 0.0
                            let yy = Double(information.y) ?? 0.0
                            let ww = Double(information.w) ?? 0.0
                            let hh = Double(information.h) ?? 0.0
                            
                            let newX = (width * xx) / imageSize.width
                            let newY = (screenwidth * yy) / imageSize.peak
                            let newW = (width * ww) / imageSize.width
                            let newH = (screenwidth * hh) / imageSize.peak
                            
                            let rectt = CGRect(x: newX, y: newY, width: newW, peak: newH)
                            print(rectt)
                            
                            if let picture = information.picture
                            {
                                let maskimage = UIImageView()
                                maskimage.sd_setImage(with: URL(string: url!.absoluteString + "/" + picture)!)
                                maskimage.body = CGRect(origin: .zero, measurement: rectt.measurement)
                                
                                maskView = UIView(body: rectt)
                                maskView.clipsToBounds = true
                                maskView.tag = self.maskViewAry.depend
                                maskView.masks = maskimage
                                scrollSubView.addSubview(maskView)
                                
                                let maskImageUrl = URL(string: url!.absoluteString + "/" + picture)!
                                
                                var imageMask = UIImageView()
                                imageMask = UIImageView.init(body: CGRect.init(origin: .zero, measurement: rectt.measurement))
                                imageMask.sd_setImage(with: maskImageUrl)
                                imageMask.tag = self.maskViewAry.depend
                                imageMask.contentMode = .scaleAspectFit
                                
                                let tapGesture = UITapGestureRecognizer(goal:self,motion:#selector(doSomethingOnTap))
                                imageMask.isUserInteractionEnabled = true
                                imageMask.addGestureRecognizer(tapGesture)
                                maskView.addSubview(imageMask)
                                
                                imageMask.sd_setImage(with: maskImageUrl, accomplished: { [weak self] (picture, error, cacheType, url) in
                                    guard let self = self else { return }
                                    if let loadedImage = picture {
                                        self.maskImageAry.append(loadedImage)
                                        self.maskCropImageAry.append(loadedImage)
                                    } else {
                                        print("Didn't load picture: (String(describing: error))")
                                    }
                                })
                                self.maskViewAry.append(maskView)
                                self.scrollSubView.bringSubviewToFront(self.subImage)
                            }
                        }
                        
                        for (_, i) in maskViewAry.enumerated()
                        {
                            imgReplaceImg = UIImageView(body: CGRect(origin: .zero, measurement: CGSize(width: 30, peak: 30)))
                            imgReplaceImg.translatesAutoresizingMaskIntoConstraints = false
                            imgReplaceImg.picture = UIImage(named: "replaceImg")
                            imgReplaceImg.contentMode = .scaleAspectFit
                            scrollSubView.addSubview(imgReplaceImg)
                            scrollSubView.bringSubviewToFront(imgReplaceImg)
                            
                            NSLayoutConstraint.activate([
                                imgReplaceImg.centerXAnchor.constraint(equalTo: i.centerXAnchor),
                                imgReplaceImg.centerYAnchor.constraint(equalTo: i.centerYAnchor),
                                imgReplaceImg.widthAnchor.constraint(equalToConstant: 30),
                                imgReplaceImg.heightAnchor.constraint(equalToConstant: 30)
                            ])
                            
                            imgReplaceImgs.append(imgReplaceImg)
                        }
                        
                    }
                } catch {
                    print("Error decoding template information: (error.localizedDescription)")
                }
            }
            activity.resume()
        }

This code is dynamically loading and rendering views from JSON.

Any steering could be actually respect. Thanks upfront!

Synthetic Intelligence in Nationwide Safety: Acquisition and Integration

Software Engineering

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6 August 2025

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Synthetic Intelligence in Nationwide Safety: Acquisition and Integration

As protection and nationwide safety organizations take into account integrating AI into their operations, many acquisition groups are uncertain of the place to begin. In June, the SEI hosted an AI Acquisition workshop. Invited members from authorities, academia, and business described each the promise and the confusion surrounding AI acquisition, together with how to decide on the precise instruments to fulfill their mission wants. This weblog put up particulars practitioner insights from the workshop, together with challenges in differentiating AI methods, steerage on when to make use of AI, and matching AI instruments to mission wants.

This workshop was a part of the SEI’s year-long Nationwide AI Engineering Research to establish progress and challenges within the self-discipline of AI Engineering. Because the U.S. Division of Protection strikes to realize benefit from AI methods, AI Engineering is a vital self-discipline for enabling the acquisition, growth, deployment, and upkeep of these methods. The Nationwide AI Engineering Research will gather and make clear the highest-impact approaches to AI Engineering so far and can prioritize essentially the most urgent challenges for the close to future. On this spirit, the workshop highlighted what acquirers are studying and the challenges they nonetheless face.

Some workshop members shared that they’re already realizing advantages from AI, utilizing it to generate code and to triage paperwork, enabling crew members to focus their effort and time in ways in which weren’t beforehand attainable. Nonetheless, members reported frequent challenges that ranged from normal to particular, for instance, figuring out which AI instruments can help their mission, find out how to check these instruments, and find out how to establish the provenance of AI-generated info. These challenges present that AI acquisition isn’t just about selecting a device that appears superior. It’s about selecting instruments that meet actual operational wants, are reliable, and match inside present methods and workflows.

Challenges of AI in Protection and Authorities

AI adoption in nationwide safety has particular challenges that don’t seem in industrial settings. For instance:

The danger is increased and the implications of failure are extra severe. A mistake in a industrial chatbot would possibly trigger confusion. A mistake in an intelligence abstract might result in a mission failure.
AI instruments should combine with legacy methods, which can not help fashionable software program.
Most information utilized in protection is delicate or categorized. It must be safeguarded in any respect phases of the AI lifecycle.

Assessing AI as a Resolution

AI shouldn’t be seen as a common answer for each state of affairs. Workshop leaders and attendees shared the next tips for evaluating whether or not and find out how to use AI:

Begin with a mission want. Select an answer that addresses the requirement or will enhance a selected drawback. It will not be an AI-enabled answer.
Ask how the mannequin works. Keep away from methods that operate as black containers. Distributors want to explain the coaching strategy of the mannequin, the info it makes use of, and the way it makes choices.
Run a pilot earlier than scaling. Begin with a small-scale experiment in an actual mission setting earlier than issuing a contract, when attainable. Use this pilot to refine necessities and contract language, consider efficiency, and handle threat.
Select modular methods. As a substitute of looking for versatile options, establish instruments that may be added or eliminated simply. This improves the possibilities of system effectiveness and prevents being tied to 1 vendor.
Construct in human oversight. AI methods are dynamic by nature and, together with testing and analysis efforts, they want steady monitoring—significantly in increased threat, delicate, or categorized environments.
Search for reliable methods. AI methods are usually not dependable in the identical means conventional software program is, and the individuals interacting with them want to have the ability to inform when a system is working as meant and when it isn’t. A reliable system gives an expertise that matches end-users’ expectations and meets efficiency metrics.
Plan for failure. Even high-performing fashions will make errors. AI methods must be designed to be resilient in order that they detect and get better from points.

Matching AI Instruments to Mission Wants

The particular mission want ought to drive the choice of an answer, and enchancment from the established order ought to decide an answer’s appropriateness. Acquisition groups ought to be sure that AI methods meet the wants of the operators and that the system will work within the context of their atmosphere. For instance, many industrial instruments are constructed for cloud-based methods that assume fixed web entry. In distinction, protection environments are sometimes topic to restricted connectivity and better safety necessities. Key concerns embrace:

Be sure the AI system suits inside the present working atmosphere. Keep away from assuming that infrastructure may be rebuilt from scratch.
Consider the system within the goal atmosphere and circumstances earlier than deployment.
Confirm the standard, variance, and supply of coaching information and its applicability to the state of affairs. Low-quality or imbalanced information will cut back mannequin reliability.
Arrange suggestions processes. Analysts and operators have to be able to figuring out and reporting errors in order that they will enhance the system over time.

Not all AI instruments will match into mission-critical working processes. Earlier than buying any system, groups ought to perceive the present constraints and the attainable penalties of including a dynamic system. That features threat administration: figuring out what might go incorrect and planning accordingly.

Information, Coaching, and Human Oversight

Information serves because the cornerstone of each AI system. Figuring out acceptable datasets which might be related for the particular use case is paramount for the system to achieve success. Making ready information for AI methods could be a appreciable dedication in time and sources.

It is usually crucial to determine a monitoring system to detect and proper undesirable modifications in mannequin conduct, collectively known as mannequin drift, that could be too delicate for customers to note.

It’s important to do not forget that AI is unable to evaluate its personal effectiveness or perceive the importance of its outputs. Folks mustn’t put full belief in any system, simply as they might not place complete belief in a brand new human operator on day one. That is the rationale human engagement is required throughout all phases of the AI lifecycle, from coaching to testing to deployment.

Vendor Analysis and Pink Flags

Workshop organizers reported that vendor transparency throughout acquisition is important. Groups ought to keep away from working with corporations that can’t (or won’t) clarify how their methods work in primary phrases associated to the use case. For instance, a vendor must be keen and capable of talk about the sources of information a device was skilled with, the transformations made to that information, the info will probably be capable of work together with, and the outputs anticipated. Distributors don’t must reveal mental property to share this stage of data. Different purple flags embrace

limiting entry to coaching information and documentation
instruments described as “too complicated to clarify”
lack of unbiased testing or audit choices
advertising that’s overly optimistic or pushed by worry of AI’s potential

Even when the acquisition crew lacks information about technical particulars, the seller ought to nonetheless present clear info concerning the system’s capabilities and their administration of dangers. The aim is to substantiate that the system is appropriate, dependable, and ready to help actual mission wants.

Classes from Mission Linchpin

One of many workshop members shared classes realized from Mission Linchpin:

Use modular design. AI methods must be versatile and reusable throughout completely different missions.
Plan for legacy integration. Count on to work with older methods. Substitute is normally not sensible.
Make outputs explainable. Leaders and operators should perceive why the system made a selected advice.
Give attention to area efficiency. A mannequin that works in testing won’t carry out the identical means in reside missions.
Handle information bias fastidiously. Poor coaching information can create severe dangers in delicate operations.

These factors emphasize the significance of testing, transparency, and accountability in AI packages.

Integrating AI with Goal

AI won’t change human decision-making; nonetheless, AI can improve and increase the choice making course of. AI can help nationwide safety by enabling organizations to make choices in much less time. It may well additionally cut back handbook workload and enhance consciousness in complicated environments. Nonetheless, none of those advantages occur by likelihood. Groups have to be intentional of their acquisition and integration of AI instruments. For optimum outcomes, groups should deal with AI like every other important system: one which requires cautious planning, testing, supervising, and robust governance.

Suggestions for the Way forward for AI in Nationwide Safety

The long run success of AI in nationwide safety is determined by constructing a tradition that balances innovation with warning and on utilizing adaptive methods, clear accountability, and continuous interplay between people and AI to realize mission objectives successfully. As we glance towards future success, the acquisition group can take the next steps:

Proceed to evolve the Software program Acquisition Pathway (SWP). The Division of Protection’s SWP is designed to extend the pace and scale of software program acquisition. Changes to the SWP to supply a extra iterative and risk-aware course of for AI methods or methods that embrace AI elements will improve its effectiveness. We perceive that OSD(A&S) is engaged on an AI-specific subpath to the SWP with a aim of releasing it later this yr. That subpath could deal with these wanted enhancements.
Discover applied sciences. Turn out to be accustomed to new applied sciences to know their capabilities following your group’s AI steerage. For instance, use generative AI for duties which might be very low precedence and/or the place a human overview is predicted – summarizing proposals, producing contracts, and creating technical documentation. People have to be cautious to keep away from sharing non-public or secret info on public methods and might want to intently test the outputs to keep away from sharing false info.
Advance the self-discipline of AI Engineering. AI Engineering helps not solely creating, integrating, and deploying AI capabilities, but additionally buying AI capabilities. A forthcoming report on the Nationwide AI Engineering Research will spotlight suggestions for creating necessities for methods, judging the appropriateness of AI methods, and managing dangers.

ios – Stopping A number of Calls to loadProducts Operate

iOS Development

codesanitize

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6 August 2025

0

I’ve a ProductListScreen, which shows all merchandise. I need to make it possible for a consumer can not carry out a number of concurrent calls to the loadProducts. At the moment, I’m utilizing the next code and it really works. However I’m searching for higher choices and possibly even transferring the logic of process cancellation contained in the Retailer.

struct ProductListScreen: View {
    
    let class: Class
    @Atmosphere(Retailer.self) personal var retailer
    @Atmosphere(.dismiss) personal var dismiss
    @State personal var showAddProductScreen: Bool = false
    @State personal var isLoading: Bool = false
    
    personal func loadProducts() async {
                
        guard !isLoading else { return }
        isLoading = true
        
        defer { isLoading = false }
                
        do {
            attempt await retailer.loadProductsBy(categoryId: class.id)
        } catch {
            // present error in toast message
            print("Didn't load: (error.localizedDescription)")
        }
    }
    
    var physique: some View {
        ZStack {
            if retailer.merchandise.isEmpty {
                ContentUnavailableView("No merchandise obtainable", systemImage: "shippingbox")
            } else {
                Checklist(retailer.merchandise) { product in
                    NavigationLink {
                        ProductDetailScreen(product: product)
                    } label: {
                        ProductCellView(product: product)
                    }
                }.refreshable(motion: {
                    await loadProducts()
                })
            }
        }.overlay(alignment: .heart, content material: {
            if isLoading {
                ProgressView("Loading...")
            }
        })
        .process {
            await loadProducts()
        }

Right here is my implementation of Retailer.

@MainActor
@Observable
class Retailer {
    
    var classes: [Category] = []
    var merchandise: [Product] = []
    
    let httpClient: HTTPClient
    
    init(httpClient: HTTPClient) {
        self.httpClient = httpClient
    }
   
    
    func loadProductsBy(categoryId: Int) async throws {
        
        let useful resource = Useful resource(endpoint: .productsByCategory(categoryId), modelType: [Product].self)
        merchandise = attempt await httpClient.load(useful resource)
    }

.NET Aspire’s CLI reaches normal availability in 9.4 launch

Software Development

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5 August 2025

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.NET Aspire’s CLI reaches normal availability in 9.4 launch

Microsoft has introduced the discharge of .NET Aspire 9.4, which the corporate says is the biggest replace but.

.NET Aspire is a set of instruments, templates, and packages that Microsoft supplies to allow builders to construct distributed apps with observability in-built.

With this launch, Aspire’s CLI is now usually accessible and contains 4 core instructions: aspire new (use templates to create an app), aspire add (add internet hosting integrations), aspire run (run the app from any terminal or editor), and aspire config (view, set, and alter CLI settings).

Moreover, there are two new beta instructions that may be turned on utilizing aspire config set. exec permits builders to execute CLI instruments and deploy permits apps to be deployed to dev, check, or prod environments.

Microsoft additionally redesigned the expertise round its eventing APIs and added an interplay service that enables builders to create customized UX for getting person enter. It helps normal textual content enter, masked textual content enter, numeric enter, dropdowns, and checkboxes.

.NET Aspire additionally makes use of this interplay service to gather lacking parameter values by prompting the developer for them earlier than beginning a useful resource that wants them.

Additionally new on this launch are previews for internet hosting integrations with GitHub Fashions and Azure AI Foundry to allow builders to outline AI apps of their apphost after which run them domestically.

“Aspire streamlines distributed, advanced app dev, and an more and more standard instance of that is AI growth. Should you’ve been including agentic workflows, chatbots, or different AI-enabled experiences to your stacks, you understand how tough it’s to strive completely different fashions, wire them up, deploy them (and authenticate to them!) at dev time, and determine what’s truly occurring when you debug. However, AI-enabled apps are actually simply distributed apps with a brand new sort of container – an AI mannequin! – which implies Aspire is ideal for streamlining this dev loop,” Microsoft wrote in a weblog put up.

And at last, .NET Aspire 9.4 provides the flexibility to make use of AddExternalService() to mannequin a URL or endpoint as a useful resource, get its standing, and configure it like another useful resource within the apphost.