A fast-track court sentenced a man and his three sons to life imprisonment for a murder at a 2017 wedding in India. The conflict arose over a music dispute, escalating to a deadly attack. The verdict followed a thorough investigation and courtroom arguments.

Table of Contents Toggle * What Is a Script in Computer Programming? Unlocking Automated Actions * Introduction: Beyond the Compile * The Scripting Advantage: Benefits and Applications * The Scripting Process: From Idea to Execution * Common Scripting Languages: A Comparative Overview * Common Mistakes to Avoid * Security Considerations in Scripting * The Future of Scripting * Frequently Asked Questions (FAQs) ## What Is a Script in Computer Programming? Unlocking Automated Actions A _script_ in computer programming is essentially a **_series of instructions_** that automate tasks, often interpreted at runtime rather than compiled into machine code. Understanding **what is a script in computer programming** is crucial for anyone looking to streamline workflows and enhance software functionality. ### Introduction: Beyond the Compile The world of computer programming is diverse, encompassing various languages and methodologies. While compiled languages like C++ and Java are crucial for creating high-performance applications, _scripting languages_ offer a different, often more flexible, approach. Understanding **what is a script in computer programming** begins with appreciating its role in automating tasks and extending application capabilities. Scripts are typically smaller and more focused than full-fledged programs, designed to be executed by another program or environment. This makes them ideal for tasks like system administration, web development, and game engine automation. ### The Scripting Advantage: Benefits and Applications Understanding **what is a script in computer programming** also involves grasping the benefits they provide: * **Automation:** Automate repetitive tasks, freeing up valuable time and resources. * **Flexibility:** Easily modify and adapt scripts to changing needs without recompiling. * **Extensibility:** Extend the functionality of existing applications and systems. * **Rapid Development:** Scripting languages often have simpler syntax and faster development cycles than compiled languages. * **Cross-Platform Compatibility:** Many scripting languages are designed to run on multiple operating systems. Scripts find applications in a vast range of domains: * **Web Development:** Dynamic content generation, form processing, and server-side logic (e.g., JavaScript, PHP, Python). * **System Administration:** Automating system maintenance tasks, managing users, and monitoring performance (e.g., Bash, PowerShell, Python). * **Game Development:** Controlling game logic, character behavior, and level design (e.g., Lua, C#). * **Data Analysis:** Processing and analyzing large datasets (e.g., Python, R). * **Office Automation:** Automating tasks in applications like spreadsheets and word processors (e.g., VBA). ### The Scripting Process: From Idea to Execution The process of creating and executing a script generally involves these steps: 1. **Define the Task:** Clearly define the task the script is intended to automate or perform. 2. **Choose a Scripting Language:** Select a scripting language appropriate for the task and target environment. Popular choices include Python, JavaScript, Bash, and PowerShell. 3. **Write the Script:** Write the script using the chosen language, following its syntax and conventions. 4. **Test the Script:** Thoroughly test the script to ensure it performs as expected and handles potential errors. 5. **Execute the Script:** Execute the script using an interpreter or runtime environment. This may involve running the script directly from the command line or embedding it within another application. 6. **Monitor and Maintain:** Monitor the script’s performance and make any necessary updates or modifications to ensure its continued effectiveness. ### Common Scripting Languages: A Comparative Overview Understanding **what is a script in computer programming** also means understanding the tools available. Here’s a comparison of some popular scripting languages: Language | Use Case | Key Features ---|---|--- Python | General-purpose, data science, web dev | Readable syntax, extensive libraries, cross-platform JavaScript | Web development, front-end, back-end | Widely supported by browsers, event-driven, versatile Bash | System administration, shell scripting | Command-line interface, powerful system utilities, automation PowerShell | System administration, Windows automation | Object-oriented, access to .NET framework, powerful for Windows environments PHP | Web development, server-side scripting | Designed for web servers, integrates with databases, widely used for dynamic websites Lua | Game development, embedded systems | Lightweight, fast, easy to embed, popular in game engines ### Common Mistakes to Avoid * **Lack of Planning:** Starting to code without a clear understanding of the task can lead to inefficient and buggy scripts. * **Insufficient Testing:** Failing to thoroughly test the script can result in unexpected errors and failures. * **Poor Error Handling:** Not handling potential errors gracefully can cause the script to crash or produce incorrect results. * **Lack of Documentation:** Failing to document the script can make it difficult to understand and maintain in the future. * **Security Vulnerabilities:** Ignoring security considerations can expose the script to malicious attacks. ### Security Considerations in Scripting When you consider **what is a script in computer programming** , security is paramount. Scripts, especially those dealing with sensitive data or system access, can be vulnerable to security exploits. Input validation, output encoding, and proper authorization mechanisms are crucial for preventing attacks like _SQL injection, cross-site scripting (XSS),_ and _command injection_. Regularly auditing and updating scripts can help mitigate these risks and ensure the integrity of the system. ### The Future of Scripting Scripting languages continue to evolve, driven by the increasing demand for automation and the rise of new technologies. Emerging trends include: * **Low-Code/No-Code Platforms:** These platforms allow users to create scripts and applications with minimal coding, democratizing access to automation. * **Cloud-Native Scripting:** Serverless computing and cloud functions enable developers to run scripts on demand, scaling resources automatically. * **AI-Powered Scripting:** Machine learning is being used to automate script generation and optimization, making scripting more efficient and intelligent. #### Frequently Asked Questions (FAQs) **What is the difference between a script and a program?** A _script_ is typically interpreted at runtime and is designed to automate specific tasks within an existing environment or application. A _program_ , on the other hand, is usually compiled into machine code and runs independently as a standalone application. Scripts often rely on other programs for their execution, while programs typically do not. **What is an interpreter in the context of scripting languages?** An _interpreter_ is a program that reads and executes a script line by line, translating the script’s instructions into actions that the computer can understand. Unlike compilers, which translate entire programs into machine code, interpreters execute scripts directly without creating a separate executable file. **What are some key features that make a language suitable for scripting?** Key features include a _simple and easy-to-learn syntax, dynamic typing, automatic memory management_ , and a _rich set of built-in functions and libraries_. The ability to quickly prototype and iterate is also crucial for effective scripting. **Is scripting only for automating simple tasks?** No, scripting is not limited to simple tasks. While scripting is commonly used for automation, it can also be used to develop _complex applications, build dynamic websites, and perform advanced data analysis_. The complexity of a script depends on the task it is designed to perform. **What is the difference between client-side and server-side scripting?** _Client-side scripting_ runs on the user’s computer within a web browser, typically using JavaScript, to enhance the user interface and interact with web pages. _Server-side scripting_ , on the other hand, runs on the web server to generate dynamic content, process form submissions, and manage data. **Can scripts be used for malicious purposes?** Yes, scripts can be used for malicious purposes. Malicious scripts can be used to _steal data, inject malware, or launch denial-of-service attacks_. It is important to implement security measures to protect against these threats. **How does debugging work in scripting languages?** Debugging in scripting languages typically involves using a _debugger_ to step through the script line by line, inspect variables, and identify errors. Debuggers are often integrated into Integrated Development Environments (IDEs) or provided as command-line tools. _Print statements_ can also be used for basic debugging. **What is the role of libraries in scripting?** Libraries provide _pre-written code modules_ that can be used to perform common tasks, saving time and effort. Scripting languages often have extensive standard libraries and a wide range of third-party libraries available for various purposes. **What are some popular IDEs for scripting languages?** Popular IDEs include _Visual Studio Code, PyCharm, Sublime Text, and Atom_. These IDEs provide features such as code completion, syntax highlighting, debugging tools, and integration with version control systems. **How can I learn to write scripts?** Start by choosing a scripting language that interests you and finding _online tutorials, documentation, and coding exercises_. Practice writing scripts to automate simple tasks and gradually tackle more complex projects. Joining online communities and participating in coding challenges can also be helpful. **What is the importance of comments in scripts?** Comments are essential for _explaining the purpose and functionality of a script’s code_. They make the script easier to understand and maintain, especially for others who may need to work with the script in the future. Well-commented code is a hallmark of good programming practice. **How do I choose the right scripting language for a project?** Consider the _requirements of the project, the target environment, and your own skills and experience_. Some languages are better suited for specific tasks than others. Research the strengths and weaknesses of different scripting languages before making a decision. Python is generally excellent for beginners due to its readability.

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In the lovely world of consulting, I find myself classifying the types of engagements I work with. While **Product / System Testing** holds a special place in my heart with the close collaboration with teams building the product together, I come often to a table where the Product is platform product, the building is configuring and specially constrained programming, and the purpose of the platform product is to enable reuse for similar kinds of IT system needs. I often call this**IT Testing**. With IT Testing, a firm belief on previous experience on the platform product at hand runs strong. That makes me particularly fascinated in modeling the similarities and differences, and insisting I can learn to test across platform products. So today I decided to take moment to explain what I have gathered so far on Platform Product Testing for Dynamics 365, SAP S4/Hana, Salesforce and Guidewire. The listing of platform products is more than these four, and I very intentionally excluded some of my lovely friends from work such as Infor and ServiceNow. What makes _testing_ of a platform product based system different is the experience of inability to tell what is a platform product problem (or feature), what is something you had control over in changing, and what comes from your rules combined with your data. For it to work for the business purpose it was acquired, the culprit does not seem like a priority. If it does not run your business the way your business needs running, there is a problem. Recognizing a problem starts then figuring out what to do with such problems. _Acceptance testing_ with business experts is essential and critical, but also very disruptive to business as usual if it needs repeating regularly. Since most of the functionality comes from the Platform Product, your integration project costs are usually optimized by focusing their testing on the things your contractor is changing and thus responsible for. This may mean that Acceptance testing sees an integrated end to end system, while other testing has been more isolated. Automation, if it exists, is the customers choice in investing in essentially multivendor feedback where some of the parts are the product that, theoretically, was tested before given to you - just not with your configurations, integrations and data that run your business. Let's talk a bit about the platform products. **Dynamics 365** , Power Platform, is a set of Microsoft Platform Products giving you ERP and CRM types of functionalities with lots of low-code promises. **Salesforce** is primarily CRM-types of functionalities, and it's a cloud-based multi-tenant platform. **SAP S/4HANA** is with ERP-types of functionalities and enough history so that the new and old mix. **Guidewire** is insurance-focused platform product. My curiosity with these started with noting vocabulary. A thing we know well in testing is a concept of a **test environment**. They come in two forms: long-running (production-like) and ephemeral. For Salesforce the environments are called _sandbox_ and _scratch org._ For SAP matching concepts to get are testing environments, development environments and the supporting tooling of _transport/ChaRM_. __ For Dynamics 365 we talk about _solution packages_ and expect understanding of containers. And for Guidewire we talk of bundles, patches and upgrades. While I recognize the dynamics of how things work in each, I get corrected a lot on use of wrong words. Each of these lovely platform products comes with its own _programming language._ Salesforce gives us Apex. Guidewire introduces us to Gosu. Dynamics drives us to low code power apps components configurations. SAP gives us ABAB and configurations. For someone who holds dear the belief that sufficiently complex configuration is programming, I find these just fascinating. My highlights so far are: Dynamics 365 * Got to love containers as an approach and Azure DevOps makes this feel to me more like modern product development for deployment tooling side. * UI automation requires understanding of user-specific settings, and I hear UI can be fragile. Locators for test automation aren't straightforward. * Test from APIs and a bit from end to end in UI. * Pay attention to solution layering discipline, and automate deployment and data seeding. * Get started: * store solutions in source control, build import/export pipelines via Power Platform Build Tools, prefer API tests and small UI smoke suites SAP S/4HANA * Automated change impact analysis relying on the structure of Transports/ChaRM is kind of cool given your tools of test management match the support. It is also generally not optional and trying other things can be trouble. * Config changes have impacts across modules and business process chain testing is essential * Get started: * map transports to test suites, automate test runs on transport promotion, use S/4 HANA test automation tool where available and treat integration flows as first-class tests. Salesforce * Multi-tenant means quota limits. Stay within the limits. Testing too big is trouble. * CI/CD and scratch orgs allow for lovely change-based test automation practice. Use mocks for integrations. * Smart scoping of data for test purposes helps, plan for data subsetting and refresh cadence. * Locators for test automation can be next level difficult for Shadow DOM and dynamic components. * Get started: * enforce Apex test coverage, minimize data creation in tests, use scratch orgs + CI for PR validation, monitor governor limits during pipeline runs. Guidewire * Product model–driven testing: insurance product model serves as testing anchor * Collected open source toolset as 'Guidewire Test Framework' and enforced rules around sufficient use of them guide the ecosystem towards good practices like test automation and coverage. * Limitations on _some contracts_ on use of AI can significantly limit hopes for use of AI * Get started: * create a policy lifecycle regression pack; adopt Guidewire Testing Framework; run regression against each product model drop; negotiate test environment refresh cadence with vendor/ops. For all _reuse of test artifacts across clients is theoretically possible._ For all, _test data management_ is necessary but execution of its practice differs: Salesforce and D365 drive synthetic data and subsetting approaches, and SAP and Guidewire require larger production-like data sets; fast refresh capability and data masking is universal. All come with a _CI/CD pipeline_ but each have a platform specific recommended one: Salesforce DX for Salesforce, Power Platform build tools + Azure Devops for Dynamics365, transport/ChaRM automation for SAP. Universal truths, maybe: * Need for **strong regression testing** due to vendor-driven releases * Presence of **custom code + configuration layer** you must retest * Requirement for **representative test data** * Complexity of **cross-module / cross-app business processes** * **Integration-heavy** test design (APIs, services, middleware) * Organizational **constraints around AI-generated artifacts** * **Upgrade regression risk** as a consistent pain point Decided this could be helpful - Platform testing capabilities and constraints comparison. At least it helps me with learning coverage as I venture further into these. Capability / Constraint| **Guidewire**| **Salesforce**| **Dynamics 365 / Power Platform**| **SAP (S/4HANA)** ---|---|---|---|--- **Metadata-driven development**| (✔️) Config layers, product model| ✔️ Core concept| ✔️ Solutions + Dataverse| (✔️) Mostly configuration, less metadata-portable **Proprietary programming language**| ✔️ Gosu| ✔️ Apex| — (PowerFx only for Canvas, but not core platform)| ✔️ ABAP **Strict platform resource limits**| (✔️) Some internal limits| ✔️ Governor limits| (✔️) API limits & throttling| (✔️) Performance constraints by module **Vendor-controlled releases with required regression**| ✔️ Product model upgrades & patches| ✔️ Seasonal releases| ✔️ Wave updates| ✔️ Transport-based releases & upgrade cycles **Automated test impact analysis supported**| —| (✔️) Through metadata diffs + DX| (✔️) Via solution diffs & pipelines| ✔️ Transport-level impact analysis **Native test automation tooling**| ✔️ Guidewire Testing Framework| (✔️) Apex tests + UI Test Builder (limited)| (✔️) EasyRepro / Playwright guidance but not “native”| ✔️ SAP S/4HANA Test Automation Tool **UI layer highly changeable / automation fragile**| (✔️) Angular-based UI, moderate| ✔️ Lightning DOM changes often| ✔️ Model-driven apps update frequently| (✔️) Fiori stable but customizable **Complex cross-module business processes**| ✔️ Policy ↔ Billing ↔ Claims| (✔️) Depends on org complexity| (✔️) Depends on app footprint| ✔️ Core ERP complexity across modules **Strong CI/CD support from vendor**| (✔️) Limited compared to others| ✔️ Salesforce DX| ✔️ Azure DevOps + Build Tools| (✔️) SAP CI/CD + ChaRM **Easy ephemeral environment creation**| —| ✔️ Scratch orgs| ✔️ Dev/Test environments via Admin Center| — (environments heavy, transports rely on fixed landscapes) **Heavy dependency on realistic test data**| ✔️ Policy & claims data| (✔️) For integration flows| (✔️) For model-driven logic| ✔️ Mandatory for end-to-end flows **Contractual constraints on AI-generated code/config**| (✔️) Vendor & client contracts commonly restrictive| (✔️) Org policies vary| (✔️) Varies by tenant/governance| (✔️) Strong compliance usually restricts **Complex upgrade regression risk**| ✔️ High| (✔️) Medium| (✔️) Medium| ✔️ Very high **Platform-driven integration patterns (APIs, services)**| ✔️ SOAP/REST internal services| ✔️ REST/Bulk/Messaging| ✔️ Dataverse APIs + Azure| ✔️ BAPIs/IDocs/OData **Stable API layer for automation**| (✔️) Good internal APIs| ✔️ Strong API surface| ✔️ Dataverse APIs stable| ✔️ Strong API layer but complex Out of all the difference, this one is the most defining: ephemeral test environments: Salesforce, Dynamics 365 AND vendor-native automation tooling: SAP, Guidewire.

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