GENETIC ALGORITHM-BASED SYSTEM FOR IMPROVING SOFTWARE SOLUTION QUALITY

Abstract

The method and system for enhancing software quality using genetic algorithms (GAs) are demonstrated in this invention. The system automates the optimization process and solves the critical software quality attributes such as performance, reliability, maintainability and scalability. The method is based on encoding software solutions as genetic representations, or chromosomes, where fitness is iteratively computed through a series of genetic operations, such as selection, crossover, and mutation. Through these operations the system explores a wide number of possible solutions in return exhaustively refining them to more high quality configurations. The system is composed of a Genetic Algorithm Processor (GAP) to control genetic operations, a Solution Encoder to map software elements into chromosomes, a Fitness Evaluator to evaluate quality according to user-defined criteria, and a Population Manager to manage the evolutions of solutions between generations. An initial population of solutions are generated, evaluated and improved through successive generation based on fitness score. In addition the invention offers great acceleration of identification of high quality software configurations compared to prior art and flexibility in use across many software domains. This method ultimately allows developers to attain higher software quality more expeditiously, consistent with the current agile development trends and responding to the intricacies of modern software engineering problems.

Specification

Description:Field of the invention
[001] The invention described herein relates to software engineering, and in particular to methods and systems for improving software quality. It uses genetic algorithms to solve software quality metrics optimization challenges such as code mathematics, software architecture, and the optimization of the testing process.
Description of Related Art
[002] As systems become more complex, demanding higher performance, reliability and maintainability, software quality has become increasingly critical in recent years. However, most approaches towards improving software quality rely on arduous manual testing, code reviews and iterative refinement, both of which take a long time and demand a lot of human expertise.
[003] While these methods can overcome many of the conventions associated with optimization, they are unable to address the highly diverse and complex nature of software systems for which there are many interdependencies and configurations over which they simply cannot optimize through conventional means alone. In addition, as development cycles shorten, it is also desirable to have automated methods that improve code quality continuously, and adapt to rapidly changing requirements.
[004] New possibilities for software optimization arise with the advent of the artifical intelligence and specifically evolutionary algorithms like genetic algorithm (GA). The genetic algorithms, based on the principles of natural selection, afford a robust means for the solution of high complex, multi-dimensional optimization problems. One of the advantages of GAs is that they can efficiently search large solution spaces and quickly find near optimal configurations through simulating evolutionary processes.
[005] By applying GAs to the task of enhancing software quality, we should be able to automate the search for configurations and code structure that optimizes some quality metric, not only functionality and efficiency, but rather, adaptability, reliability, and possibly other desired qualities.
[006] The underlying premise of this invention is that, by encoding software elements into a genetic algorithm structure, according to the continued and automatic improvement of software solutions. Genetic operators-selection, and crossover, and mutation-are iteratively applied to narrowly refine a population of software solutions toward higher-quality configurations.
[007] This method allows developers to improve software quality targeted attributes with least manual effort by adapting fitness functions to preference desired attributes. In modern software development environments such an approach is crucial, especially in terms of agile methodologies that require frequent incremental improvements. Also contributing to the software development process, this invention proposes a novel system for systematic improvement of software quality, meeting the complexity of modern software applications, and keeping pace with a modern development practice.
SUMMARY
[008] This invention implements a software quality enhancement using genetic algorithms (GAs). The system utilizes the evolutionary principles of selection and crossover and mutation to iterate to make software solutions optimize certain quality attributes such as performance, reliability, maintainability, and scalability.
[009] We start by encoding potential solutions (for example code structures, algorithms or architectural configurations) in what are called genetic representations - i.e. in chromosomes. Across subsequent generations, a population of these encoded solutions undergoes genetic operations in order to produce higher quality solutions during each generation.
[0010] Genetic algorithm processor (GAP) is responsible for genetic operations; solution encoder converts software elements into chromosomes; and fitness evaluator estimates the quality of solutions; finally, population manager maintains the solution population.
[0011] The system has this setup for automatically ranking software characteristics according to predefined fitness criteria, continually improving the solution with almost no manual effort.
[0012] Each solution is evaluated based on particular fitness functions (customizable to satisfy disparate software quality goals), and higher-scoring solution are selected for reproduction.
[0013] It generates offspring solutions through crossover and mutation, blending selected traits from high fitness candidates and injecting some variability to explore new areas. Then this iterative process goes on until an optimal (or satisfactorily good) solution comes up or until we come to maximum number of generations.
[0014] The software quality optimization based on the GA approach provides advantages in order to speed up identification of high quality configurations over traditional methods. Having a typically universal applicability to a range of software types; from embedded systems, to large scale enterprise applications, the invention fits well with the practice of agile development.
[0015] where rapid iteration and iteration in a continuous improvement cycle are as important as they are. Later on, this invention creates an automation of this invention to do the software solution enhancement that saves a lot of time on the development side, and brings into better quality than with less time investment.
BRIEF DESCRIPTION OF THE DRAWINGS
[0016] FIG. 1 A schematic diagram of genetic algorithm for improvement of software solution
DETAILED DESCRIPTION
[0017] In this invention, a comprehensive system and technique for automating the process of optimizing various quality metrics, including efficiency, reliability and adaptability, such as via genetic algorithms (GAs), are provided. The system was designed in a way that can encode different aspects of software solutions into genetic structures or what we call 'chromosomes' to explore complex solution spaces in an efficient way.
[0018] The system is comprised of key components: In this paper, we use a Genetic Algorithm Processor (GAP), a Solution Encoder, a Fitness Evaluator, a Population Manager, and a Solution Repository. The GAP is the basic unit responsible for applying genetic operations (selection, crossover, and mutation) to create new generations of promising software solutions.
[0019] It does the work of iterative refinement locking the population into better quality. The Solution Encoder processes elements of the software (for example code modules, configurations, or architectural structures), and maps them to the format that can be processed by genetic algorithms as "genes" inside of chromosomes.
[0020] But as a crucial role, the Fitness Evaluator sets a quality score for each software solution based on predefined user measure. The fitness criteria may vary based on the type of quality you want in the final product of the software, such as run time, error rate, code maintainability and resource efficiency. If the fitness score is higher, it will be used to prioritize for selection and have hope of producing better offspring through genetic operations.
[0021] The Population Manager maintains a diverse set of solutions for each generation so as to prevent local optimization and to provide comprehensive exploration of the solution space. By the way of insertion of crossover and mutation operators, the population is allowed to evolve creating new hybrid solutions.
[0022] Mutation are random changes, which is essentially a way to take a saw and break it, unlike crossover, which combines sections of two parent solutions to create offspring that have characteristics from both; crossover is a type of mutation in evolutionary algorithms, where mutation is a process of randomly 'doubling' the chromosomes, essentially taking a saw and breaking it.
[0023] The Solution Repository stores solutions for each generation so developers can see progress and revert back to an earlier version if needed. It also functions as a knowledge base containing techniques for benchmarking of previous solutions as well as reusing successful configurations.
[0024] The system operates iteratively: A first value of a population of software solutions and its assessment of fitness is produced. New solutions are created from combinations of high fitness solutions, each generation taking the best of a previous, selective and creative iteration, moving inexorably closer to the optimal configuration. Stopping condition can be set to stop the process when a predefined constraint for the fitness value or maximum number of generations is reached.
[0025] The resulting approach accelerates the software development lifecycle by eliminating manual code review, rapidly prototyping and testing, and being able to automate quality improvements. This invention is suitable for a variety of software types from the small of an embedded system to large scale application to produce high quality software in reduced time as well as in terms of resource allocation.
, Claims:1. I/We Claim: A method for improving software quality using a genetic algorithm, wherein software components are represented as chromosomes, comprising the steps of:
a. Encoding the software solution's elements into a population of chromosomes,
b. Iteratively applying genetic operations including selection, crossover, and mutation on said population,
c. Evaluating each software solution based on a fitness function that reflects desired quality criteria, and
d. Selecting high-fitness solutions to form successive generations, thereby converging on an optimized software configuration.
2. I/We Claim: A system for evolving software solutions to enhance quality metrics.
3. I/We Claim: A solution repository for storing evolved software solutions across generations
4. I/We Claim: The method of claim 1, wherein the fitness function is dynamically adaptable based on real-time performance data.
5. I/We Claim: The system of claim 2, wherein the mutation operation selectively modifies specific software characteristics to explore improvements in performance and reliability.
6. I/We Claim: he method of claim 1, further comprising the step of storing high-fitness solutions for benchmarking and potential rollback during software deployment.

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