Transforming Software Development with AI: Top Applications You Need to Know — Part 1

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Introduction

The field of software engineering has existed for ages. In recent years, developments in AI have witnessed an upsurge in terms of tools and the usage of multiple applications. Many established companies are now incorporating their focus on developing intelligent systems to enhance the traditional phases of software development.

Software Engineering always faces persistent challenges across its lifecycle, from estimation difficulties and unclear requirements to resource-consuming testing and reactive maintenance. It becomes increasingly necessary and clear that AI techniques are a game changer that reshapes how we approach these issues.

This blog explores how AI addresses these challenges, analyzes the problems, and showcases its applications and innovations in estimation, testing, requirements gathering, design, and more.

Challenges in Software Engineering

Despite technological advancements, software engineering continues to grapple with critical pain points:

1. Estimation: Accurately predicting project timelines and costs is a daunting task.
2. Testing: Ensuring software quality consumes up to 50% of resources and time.
3. Requirements Gathering: Ambiguities and unclear user needs lead to costly mistakes.
4. Maintenance: Reactive approaches often fail to prevent downtime effectively
5. Collaboration: Ineffective communication among team members hinders progress.

These issues demand potential solutions — ones that AI is uniquely equipped to provide.

Requirements Gathering and Planning

Requirement analysis is the first-ever stage in the software development lifecycle process and forms the basis of building a software system. They describe the overview of the system, outlining its main objectives, goals, and scope.

The Institute of Electrical and Electronics Engineers (IEEE) defines a requirement as a condition or capability needed by a user to solve a problem or achieve an objective, which must be met by a system to satisfy a contract, standard, specification, or other formally imposed document.

They dictate how it must behave, the required qualities and the characteristics it must exhibit, etc. The key challenges with requirements engineering are

• Unclear Specifications: Misunderstandings can lead to unwanted software
• Scalability: Managing large, messy data is time-consuming
• Costly Mistakes: Poor requirements can lead to expensive problems later

Solution 1 : Semi Automatic Approach (NLP)

As software systems grew in complexity, there was an increased demand for AI-driven approaches to accentuate the efficiency of this phase. Leading researchers explored aspects of a process-oriented specification language to create specifications without formulation tools.

The solution was to introduce a blend of informality and structured design. Instead of requiring exhaustive and complete descriptions upfront, this idea proposed partial descriptions that leverage context-aware, computer-based tools to construct well-formed specifications dynamically. Objects were defined completely through their relationships with other objects and supported by a set of primitive operations to map these relations. This approach enabled objects and operations to be modeled almost exactly as a user desires

Since the specifications were formal and incomplete, creating operational specifications was challenging and error-prone. However, the benefit of informal specifications was their precision, which guided development, reduced user training, and made the system easier to maintain.

Figure 1 : Intelligently Processing Requirements Text

A semi-automated approach using NLP has been introduced to speed up requirement analysis. It can analyze text patterns and extract meaningful information. This is a form of supervised learning approach that classifies the features as actors and use cases. The output appears as use cases in subject-verb-object notation. These results are then used to create use-case diagrams

Solution 2: NL-OOPS with LOLITA

(Natural Language — Object Oriented Production System) with (Large Scale Object-based Linguistic Interactor, Translator and Analyzer)

The principle of NL-OOPs follows an attempt to identify actors and use cases using a probabilistic classification model with minimal assistance using a Rule-based approach. The resultant use cases are crisp and consistent, regardless of the size of requirements.

This technique is then supported with the tool LOLITA, an NL processing system to support object-oriented analysis. The knowledge captured in documents along with the Knowledge base in LOLITA helps to generate requirement models. This approach assumes requirements are often written in natural language (NL).

The algorithm uses a filter, extracting entities and events from its knowledge base to identify relevant classes and associations. This makes it easier to create structured models from informal unstructured text.

Solution 3: Multi-Objective Evolutionary Algorithm (MOEAs)

A crucial challenge in RE is the selection of requirements for the next software release. The optimization of this process is the core ideology of the Next Release Problem (NRP). This is an NP-hard problem that involves balancing 2 objectives:

• minimizing development costs
• maximizing client satisfaction

Traditional optimization methods currently struggle with this complexity, thus making MOEAs a better fit. These can handle multiple conflicting goals simultaneously without relying on external adjustments, unlike the traditional methods.

The complexity of this problem increases when interactions between the requirements are modeled into the Multi-Objective NRP (MONRP). A potential solution to address this is to use a multi-objective Teaching Learning Optimization (TLBO) algorithm to solve real-time instances of NRP.

Figure 2: Teaching Learning Based Optimization

This is a type of Swarm algorithm that takes inspiration from the idea of classroom dynamics. It uses the behavior of participants in a classroom to solve the requirements selection problem. It adapts to real instances based on data provided by experts. Through multiple experiments, the modified TLBO proves more effective than other methods for solving this problem.

Software Design

Software design is the activity of fabricating an engineering representation of a discussed software im

Software design is the process of conceptualizing how a software system will work before it is implemented or modified

In the process, the requirements specification or document is translated into design models that define the data structures, system design, interfaces, and multiple components. Although it only takes up about 6% of the software budget, is critical because flaws in design can have significant impacts on subsequent phases like development and testing.

The traditional software design process is

• Time-consuming
• Error-prone
• Inefficient for modern, complex systems

Solution 1: Knowledge-Based System (KBS)

An AI solution finds its applications in database design through a Knowledge-Based System (KBS). A study proposed a strategy for self-adapting to dynamic software architectures and applying these modifications to autonomous agents based on automatic computing.

Figure 3 Architecture of Autonomous Agents

How it works:

• Architectural Reference Model: This is the blueprint for the software architecture and defines the overall structure, components, and their relationships
• Desired Architectural Model: It represents the desired state of architecture, involving the user requirements from the specification.
• Planner: This component is responsible for managing the changes to achieve the DAM. It considers ARM as the initial state and devises strategies for transitioning towards the DAM.
• Component Manager: Manages the lifecycle of the components, like the creation, updating, and deletion.
• Connector Manager: Supervises the connections and coupling between the components.
• Sensor: Observes the system for any changes, anomalies, or deviations from the target state
• Effector: Takes the necessary actions to modify the software architecture based on input from the Sensor and the instructions from the Planner

Workflow:

1. Change Request: A change request or a requirement is fed into the system.
2. Sensor: The sensor detects this change and informs the Planner
3. Planner: The Planner analyses the request, evaluates the impact on the current architecture, and formulates a plan to effect changes toward DAM.
4. Component and Connector Managers: The planner collaborates with these 2 managers to implement changes, analyzing the connections and interactions through modifying, adding, or removing components.
5. Effector: This executes the changes to the software system, modifying the code base, configuration, and infrastructure as necessary.
6. Monitoring and Feedback: The Sensor continues to monitor the system for any unintended side effects or new changes that were accounted for.

Solution 2 : AI Planning in Service-Oriented Architecture (SOA)

Service-oriented architecture (SOA) is a popular design paradigm choice for the development of distributed enterprise web applications. It promotes the reusability and integrability of software in dynamic environments. This approach is effective in discovering and composing services that are already implemented on Internet platforms.

AI planning is a technique that focuses on generating a sequence of actions to transition from an initial state to a goal state. It handles dynamic, uncertain, and distributed environments effectively.

Let’s look at the mechanism of how AI planning helps in SOA:

1. Service Chaining: AI identifies services that fulfill unmet preconditions for required services.

2. Workflow Generation: It then creates abstract workflows based on service capabilities and constraints. The workflows are then transformed into executable processes using standards like BPEL

3. Optimization of Quality of Service (QoS): Evaluates multiple service alternatives to optimize attributes like Performance, Cost Effectiveness, etc.

As a benefit of employing AI Planning in SOA, it brings:

• Automation: Minimizes manual effort in composing services.
• Flexibility: Dynamically adapts to changing requirements
• Scalability: Efficiently handles large repositories and complex workflows.
• Enhanced QoS: Optimizes workflows for better performance and cost.

Conclusion

AI is redefining software engineering in all the phases of the development lifecycle. Many solutions aim to enhance automation, adaptability, and efficiency in dynamic environments.

While this part focused on the early phases of development, there is another Part 2 :) That will explore how AI is transforming the later stages, including testing, implementation, maintenance, etc.

Stay tuned to uncover how AI continues to drive innovation across the entire SDLC.