What is software development?

Software development is the process programmers use to build computer programs. The process has several stages known as Software Development Life Cycle (SDLC) which is a method for building products that meet technical specifications and user requirements.

The SDLC gives an international standard that software companies can use to build and improve effective products within a clear budget and timeline.  It includes a clear structure for development teams to follow in the design, creation, and maintenance of high-quality software. 

Man doing coding on computer | Upplex Tech

KEY STEPS IN THE SOFTWARE DEVELOPMENT PROCESS.

6 major steps in the software development life cycle, are: 

  1. Needs identification

 

This is usually the first step in building the mobile app.  At this stage, you will need to identify the viability of your product through extensive market research and brainstorming sessions.  This can be done in several ways, such as getting feedback from potential and existing customers and surveys.  Developers will need to identify the functions and services of the software that is required for the end users to reach their target and get the best experience out of it. 

The IT team and other divisions of the company should discuss the strengths, weaknesses, and opportunities of the product.  The further development phase should only be initiated after you are confident that the product fulfills every factor necessary for its success. 

  1. Requirement analysis

 

The second stage of development is requirement analysis.  At this stage, the developers, users, testers, project managers, and quality assurance are involved.  This is when the detailed outline of every component, the scope, the tasks of developers, and testing parameters are identified and laid down ensuring a quality product will be delivered.

At this stage, the stakeholders agree on the technical and user requirements and specifications of the proposed product to achieve its goals.  Programmers will also choose the software development approach such as the waterfall or V model.  This is recorded in the Software Requirement Specification document by the team which teams can always consult or refer back to during the project implementation. 

  1. Design

 

The third stage of the software development process is the design.  At this point, the developer will draw the advanced technical specifications they need to create the software to requirements.  This is also where the stakeholders can start discussing the risk levels, team composition, applicable technologies, time, budget, project limitations, method, and architectural design. 

The Design Specification Document (DSD) lists down all the aspects of the design, such as the architectural design, its components, communication, front-end representation, and user flows of the product.  This will be the manual for developers and testers to follow to reduce the chances of issues and delays in the finished product. 

  1. Development and implementation

 

At this stage, the development and implementation of the design parameters start to take place.  The coding begins based on the product specifications and requirements agreed upon in the previous stages.  The front-end developers build interfaces and back-ends while database administrators create relevant data in the database.  The programmers will run tests and review each other’s code. 

Once this is complete, you will have the pilot version to be tested.  The developers will deploy the product to an environment in the implementation stage.  The performance of the software will need to match all the requirements listed.

  1. Testing

 

At this testing stage, the software is being tested and checked for bugs and any forms of vulnerability in its performance before it is delivered to the users.  Expert testers inspect the product’s functions and make sure they are in accordance to the requirements analysis document. 

These testers will use exploratory testing or a test script to validate the performance of individual components of the software.  If there is any issue the testers will notify the developers, of which the developers are to confirm the flaws are valid and rectify or improve the program.  This process will be repeated until the software is free of bugs and performs according to requirements. 

  1. Deployment and maintenance

 

Once all tests are clear and the software is free from any defects, the developers can now deliver it to the customers.  The IT software development company will need to create a maintenance team to manage any issues encountered when using the product. 

RPA concept with blurry hand touching screen | Upplex Tech

TYPES OF SOFTWARE

There are three main groups for software depending on their use and application.  Here are the groups:

  • System Software

 

This is also called Operating System or OS, which is the program your computer uses to translate input commands into machine-readable language.  The operating system controls the computer’s hardware components. 

The popular operating systems today would be Windows OS from Microsoft, macOS for Apple MacBook, and Linux-based Ubuntu.  Web servers use the Apache OS while the UNIX system is used to build proprietary systems. 

  • Application Software

 

This is used for task performance in computers and smartphones, such as word processing apps, internet browsers, media players, photo editing tools, anti-virus, and even software-as-service (SAS) products. 

  • Programming Languages

 

This is the programming language used by coders to create programs and software.  Programming languages include Java, C++, PHP, and Simlab.

Check out other blogs

Data Management

Data administration

Data administration 🗄️ What is Data Administration? Data Administration is the process of managing data as a valuable resource. It involves setting policies, procedures, and standards for how data is created, maintained, secured, and used within an organization. The goal is to ensure that data is accurate, accessible, consistent, and secure across all systems. Contact Us ⚙️ The Data Administration Process Data Policy and Strategy Development Every data administration plan starts with setting: Data governance policies (rules and responsibilities) Data usage policies (who can access what) Security protocols (encryption, firewalls) Compliance standards (GDPR, HIPAA, etc.) These serve as the foundation for managing all data activities. Data Inventory and Classification This step involves: Identifying all data assets across the organization Classifying data by type and sensitivity (public, internal, confidential) Creating a metadata catalog for easier data discovery This helps in understanding what data exists and how it should be handled. Data Quality Management nsuring data is: Accurate (free from errors) Complete (no missing fields) Consistent (same format across systems) Timely (updated regularly) Techniques like data profiling, validation rules, and data cleansing are used to maintain quality. Database Administration Data administrators often work closely with DBAs (Database Administrators) to: Design and manage databases and data warehouses Set up backups, indexes, and performance tuning Monitor storage usage and plan for scalability They ensure that the technical side of data storage is efficient and reliable. Data Access and Security Management of the core duties is protecting data from unauthorized access: Implement role-based access control (RBAC) Use encryption at rest and in transit Monitor access logs and set up alerts for unusual behavior Manage user permissions and audit trails Data Lifecycle Management Data doesn’t live forever. Administrators handle: Data archiving for older, inactive data Retention policies to define how long data is stored Data deletion procedures to securely remove obsolete data This helps in reducing storage costs and meeting legal compliance. Monitoring and Continuous Improvement Ongoing monitoring ensures everything runs smoothly: Track data performance metrics (availability, error rates, latency) Review data access reports Regularly audit for compliance and quality Based on findings, processes are updated to improve efficiency and reliability.

Read More
cloud edge computing technology concept 600nw 2422035957

Data Migration

Data Migration 🔄 What is Data Migration? Data Migration is the process of transferring data from one system, format, or storage type to another. It’s a critical step in many IT projects such as system upgrades, cloud adoption, or database replacements. Done right, it ensures data integrity, business continuity, and minimal downtime. Contact Us ⚙️ Data Migration Process: Step-by-Step Planning and Assessment Before any data is moved, the first step is to: Understand the source and target systems Define the scope, timeline, and goals Identify potential data quality issues Assess data volume and format compatibility Data Profiling and Mapping This step involves analyzing and preparing the data: Identify data types, formats, relationships Create a mapping document that defines how each field from the source maps to the target system Detect and plan to fix inconsistencies, duplicates, or obsolete data Data Extraction In this stage, data is extracted from the source system using: SQL queries, export scripts, or ETL tools Data can be structured (from databases) or unstructured (from files, logs) Data Transformation Once data is extracted, it is transformed to fit the new format: Convert field types (e.g., string to date) Normalize data (standardize formats like dates or phone numbers) Apply business rules (e.g., currency conversions, code translations) This is also where data cleaning occurs. Data Loading After transformation, data is loaded into the target system: Load in batches or through streaming (real-time) Use tools like AWS DMS, Azure Data Factory, or custom scripts Validate that all records were transferred correctly Testing and Validation This is one of the most important steps: Compare data from source and target to ensure accuracy and completeness Run tests for data integrity, performance, and application behavior Fix any mismatches or errors found during testing Go Live and Monitoring Once data is verified: Perform the final migration or cutover Monitor the system for issues like latency, data loss, or system crashes Set up logging and alerts to catch any anomalies early

Read More
AdobeStock 761831482 Preview

Data Engineering?

Data Engineering 🏗️ What is Data Engineering? Data Engineering is the process of designing, building, and managing systems that collect, store, and convert raw data into usable formats for data analysis and business intelligence. While Data Science focuses on analyzing data, Data Engineering provides the infrastructure and tools needed to make that analysis possible. Contact Us ⚙️ The Data Engineering Process Understanding Data Requirements Before any system is built, data engineers need to understand what kind of data will be used, where it comes from, and how it will be used. This involves working with data scientists, analysts, and business teams. Data Ingestion In this stage, data is collected from multiple sources like APIs, databases, IoT devices, or files. There are two main types of ingestion: Batch Ingestion: Data is collected at intervals. Real-Time Ingestion: Data is streamed continuously (e.g., user clicks, IoT sensors). Data Pipeline Development A data pipeline is a system that automates the flow of data from source to destination. This includes: Extracting data from source systems Transforming it into the correct format Loading it into storage systems (ETL/ELT process) Data Storage & Warehousing Data engineers store data in structured formats so it can be easily queried. Popular storage solutions include: Data Lakes: For raw, unstructured data (e.g., AWS S3, Azure Data Lake) Data Transformation & Cleaning Raw data needs to be cleaned and transformed into usable formats. This step includes: Removing duplicates Handling missing values Formatting data correctly Joining data from different sources Data Orchestration & Automation Using tools like Apache Airflow or Prefect, engineers schedule and monitor data workflows to ensure timely and reliable data delivery. Data Security & Governance Data engineers also ensure the data is: Secure (using encryption, access control) Compliant with data privacy regulations (like GDPR) Well-documented for easy understanding and traceability 📈 Why Data Engineering Matters Without proper data engineering: Businesses can’t trust their data. Data scientists waste time cleaning and finding data. Real-time insights become impossible. Data engineering ensures that clean, reliable, and fast data is always available for analytics, reporting, and AI models.

Read More
AdobeStock 262173764 Preview

🧠 What is Data Science?

🧠 Data Science 🧠 What is Data Science? Data Science is a multidisciplinary field that focuses on extracting meaningful insights from data. It combines elements of statistics, computer science, and domain knowledge to collect, clean, analyze, and visualize data to support better decision-making. From healthcare to finance to e-commerce, data science is transforming the way industries operate. Contact Us 📊 The Data Science Process Understanding the Problem Every data science project starts with a clear understanding of the business or research problem. This step defines what you want to achieve and how data can help solve the issue. Data Collection The next step is gathering relevant data from various sources such as internal databases, online APIs, surveys, or sensors. This data forms the foundation of your analysis. Data Cleaning Raw data is often messy — it may have missing values, duplicate entries, or errors. Cleaning the data ensures it is accurate and ready for analysis. Data Exploration and Visualization This step involves exploring the data to identify patterns, trends, and relationships. Visualization tools like graphs and charts help make sense of complex datasets. Modeling Using machine learning algorithms such as Linear Regression, Decision Trees, or Neural Networks, data scientists build models to make predictions or automate decisions based on the data. Model Evaluation Once a model is built, it needs to be tested. Evaluation metrics like accuracy, precision, recall, and F1-score are used to assess how well the model performs. Deployment After evaluation, the model is integrated into a real-world system — like a recommendation engine on an e-commerce website — where it starts providing value to users or the business.

Read More
error: Content is protected !!
×