Software Quality

1. Introduction
Software quality is gradually picking up now days. It is new days no more luxury rather than optional requirement. When a component, process or system fulfills the requirements, needs or expectations of customer then the degree is called SOFTWARE QUALITY. To increase the Quality and Reliability of the software it's need to be tested by various algorithms and methods. Achieving the quality standards of the software, we must have known the most incorporable factors and parameters of the software. These are the parameters that add up the quality of software are:-

i. Capability (functionality) - Functionality describes the amount of function which is contained by a product or software. The main purpose of developing software is to meet all the functional requirements. All the customer requirements and needs or business requirements is contained by functional requirements. Basically functional requirements define for what purpose Software is being developed.
ii. Usability- Software Usability is a combination of understandability, learn ability, operability and last the attractiveness of the product to the last user. Usability depends on these factors- comfort level, ease to use, simplicity etc.
iii. Performance- By software performance we analyze the act of performing that software is work properly or not. These factors like communication failure, poor bandwidth, or components failure affected the performance of the software. The parameters for performance evaluation are- execution time, service unit reduction, idle time reduction etc.
iv. Maintainability- Software Maintainability provides the capability to modify the software product. Modification of software contains corrections, improvements or adaptation to change in requirements and environments.
v. Durability- Software usability helps to amend the Software Durability. It has 2 parts- Data Durability and Session Durability. To heightened durability of the software we use technologies like Data Replication and Data Repair.
vi. Serviceability- The ability to offer commit services by the software or application is called Serviceability. For the software its helps in terms of technical help, user manual, problem devolvement.
vii. Availability- By availability we analyze of how probably the system is ready for use. Availability provides the repair or restart time into account.
viii. Complexity- It has 2 types-
' Apparent Complexity- When a design or implementation is typical to comprehend and verify then that type of complexity is called Apparent Complexity.
' Inherent Complexity- These are the factors like the number and intricacy of interfaces and the number and intricacy of conditional branches are responsible for Inherent Complexity.

Keywords: Quality Function Deployment, Messy Genetic Algorithm, Usability, Reliability.

But one of the strongest criticisms of current customer is that they are unable to express their needs and expectations for the product or software. So it is very complicated to identify and list these most incorporable factors and parameters from various factors and parameters.

In Software Engineering, Quality Function Deployment is a method that capable to find those software parameters which plays major role to achieve the quality standard for software. By implementing the Quality function Deployment in various parameters and user requirements Software organizations become capable to identify the prioritized customer requirements and software parameters. But after applying QFD we are not able to identify all the software quality parameters and factors. So to increase the percentage of identification of software quality parameters we will use Messy Genetic Algorithm which is the variant of Genetic Algorithm.

In the evolutionary algorithm set Genetic Algorithm is a multipurpose and most powerful optimization tool which is model a principle of evolution. Genetic Algorithm starts from initial set of solution and use a biologically inspired evolution mechanisms to derive new and possibly better solution. By applying the Quality Function Deployment in various software parameters we get the prioritized customer requirements and Software quality parameters then we apply the Genetic Algorithm on these prioritized factors to find the appropriate quality parameters for the quality standard of software.
Messy Genetic Algorithms (Goldberg et al. 1989) are well outfitted to that type of software quality parameters selection problems .Messy Genetic Algorithms allow variable length strings that may be underspecified or over specified with respect to the problem being solved. A messy gene is a pair of (Gene Number, Allele Value). The messy chromosome is a collection of messy genes.
In order to mutate these parameters we use Messy Genetic Algorithm and by applying Quality Function Deployment we analyze the actual gap between requirements of the customer and what can company provide.
So the combination of Quality Function Deployment and Messy Genetic Algorithm help us to identify the appropriate software quality parameters to achieving the quality standard of the software and the combination of Quality Function Deployment would be a useful tool for managers of large software project.

2. Related Work

The paper by Yonghua Zhou YuliuChen [6] on 'QFD-based Decision-making Approach for Strategic BPR' derived house of quality to make the top-level decision-making strategy take both satisfying the requirement of external customer and settling the internal problems into account. We are inspired by the work of Hashem [1], M.M.A. which highlights the Global optimization through a new evolutionary algorithm. TIAN Na, CHE A-da [7] in his paper 'Goal Programming in Quality Function Deployment Using Genetic Algorithm' clubbed the methods QFD and Genetic Algorithm. We have used this relation to identify the software quality parameters. K.Y. Chan1, T.S. Dillon1, C.K. Kwong2 and S.H. Ling [8] proposed that GP based method produce a more accurate and interpretable models than the other commonly used methods like QFD. They show this on his paper 'Using Genetic Programming for Developing Relationship between Engineering Characteristics and Customer Requirements in New product'. Norberto Eiji Nawa and Takeshi Furuhashi [2] used bacterial evolutionary algorithm to discover Fuzzy system parameters. The paper 'Feature Selection and Clustering in Software Quality Prediction' by Qi Wang, Jie Zhu, Bo Yu [9] presents a new software quality prediction model based on genetic algorithm (GA) in which outlier detection and feature selection are executed simultaneously. Antonio Gonzalez and Francisco Hemera[3] worked on Iterative Rule Learning Approach in 1997.The concept of Fuzzy logic techniques are utilized in software reliability engineering. This concept is given by Xu, Z in 2001. Yi. Liu, Taghi M.Khoshgoftaar [12] in Genetic Programming Model for Software Quality Classification compared the two methodologies LRM and GP and shows that GP model is much better than LRM. S. Keshavarz and Reza Javidan [13] deals with Software Quality Control with criteria of covering application and proposed a new method based on genetic algorithm for generating optimal test data. In this paper 'Comparison of Software Quality Models: An Analytical Approach' Sanjay Kumar Dubey, Soumi Ghosh, Prof. (Dr.) Ajay Rana [11] analyze the qualitative characteristics and side-by-side determine the software quality. Paper by Salah Bouktif, Bal??azsK??egl, HouariSahraouiIn [14] combines Software Quality Predictive Models. We are inspired by the work on Combining Models because combined models works well on the particular system or in the particular type of organization. David A and Gary B. Lamont [5] focus on Messy Genetic Algorithms for Multi Objective Optimization. We are inspired by the work of Linda Murphy, Hoda S. Abdel-Aty-Zohd , M.Hashem-Sherif[10] they tracked the Genetic Algorithm Model for Product Deployment in Telecom Services. They have used the genetic algorithm to update the parameters applied to the input measurements to find the optimum solution for the defect tracking model system. Hillol Kargupta [15] introduces the gene expression messy genetic algorithm (GEMGA). It directly searches for relations among the member of the search space. We have used the Messy Genetic Algorithm to find the appropriate customer requirements and engineering characteristic in our paper. Lastly the paper by Kohei Arai [16] compares the conventional simple Genetic Algorithm to Messy Genetic Algorithm. This highlights the importance of Messy Genetic Algorithm for clustering. We have extended the concept of indexing from messy genetic algorithm to identify the highly prioritized customer requirements and engineering characters.


3. Our framework

To highlight the Quality Parameters we have formed two student's team developing Module-1 and Module-2. These team are assigned a task of developing a website of educational institute. They are told about the basic functionality which is to be embedded in the website. These are basically selected to navigational guidance like providing the basic user interface and task selected documentation. The basic categorization of the parameters is: (adapted from Rick Sobiesiak and Tim O' keefe)
a) Context Shift: It refers to paradigm shift when a user changes from standalone systems to Web based system. Than how easily it can migrate from one system to another.
b) Navigational Guidance: It refers to the support provided to user on step-by-step basis. Like Intelligence in Microsoft.
c) System Feedback- It is an instant feedback which the user service while performing any action.
d) Error Feedback- It is the system's response when user encounters an error. It provides help information and troubleshooting guidance.
These four categories of Voice of Customers are linked with Usability and Reliability features which the software companies can offer. In order to link between the voice of customer (Customer's Requirement) and what the company can provide.
By using the Quality Function Deployment we are develop a relationship between 'What' the customer wants and 'How' the software industry will achieve. We start with finding out the explicit and implicit customer requirements and write it on the Horizontal Partitions of QFD. On the courtesy the Technical Factor's which the company can offer are given by Vertical Partitioning of features like 'Usability' and 'Reliability'.
The relationship can be given by developing the Competitive Assessments which includes customer competitive assessment and Technical Competitive assessment which the company can give in their product or do necessary amendments.

From the Figure.1 we are unable to sort out the Voice of Customer and Prioritized Technical Descriptor, under voice of customers 'Context Shift' and 'Navigational Guidance' is having the highest score so we select these two as the highest priority factors. Similarly 'Understandability', 'Operability' and

Figure1: QFD

'Optimizing' features are taken as high priority delivery. We can write them as two sets:

Understandability Operability Optimizing

Prioritized Voice of Customer
Context Shift Navigational Guidance

Prioritized Technical Descriptors

Table 1: Two sets of Descriptors
To find out the best combination of voice of customer and prioritized technical descriptor we use Messy Genetic Algorithm feature. The reason for using Messy Genetic Algorithm is it uses variable length and position independent coding. Even if we interchange the sequence of the two parameters the output remains unaffected.
The two Teams 1 and 2 made the website of a university. Based on the information How and Usability it

Simple Page make by Team 1

Complex Page make by Team 2
Figure2. Comparison of website pages makes by Teams 1 & 2
was given to an independent student's team to rate it according to various parameters given in the Questionnaire.

4. Analysis
Starting with the Genetic QFD phase we applied the relationship given between 'Voice of Customer' and 'Prioritized Technical Descriptors' into set of relational rules. For e.g.: 'Understandability' and its relationship with 'Context Shift' and 'Navigational Guidance' under one set of Rule Chromosome.

Understandability UC UC UO UO
Operability OU OC

Optimizing OC OU
Context Shift

Navigational Guidance

Rule 1 Rule 2 ''''.. Rule N

Table2. Rule defining the ratio of Relationship in Fuzzy Linguistic terms.

Enable Bits


0 1 0 '' 0 1




1 2 3 4


1 & 3
The Cut & Splice Operation

4 & 2

Table3. Messy Coding and positional preferences Using Cut and Splice operation.


The key idea is to generate an index to the gene which allows identifying its position. The crossover operator is replaced by more general cut and splice operator allowing the parents to make with different lengths. The idea is to cut and splice for both parents independently and to splice the four fragments. After successive mutations it was found that 'Understandability' and 'Navigational Guidance' emerged as the most perfect outcome of Messy Algorithm.
From the Questionnaire and Stream listing of the websites, we found that Team 1 was rated higher than Team 2. The Parameters on which the teams give due weightage were 'Understandability' and 'Navigational Guidance' in term of Ease of Use.
Thus we can say that the results generated using QFD and Messy Algorithm exactly matched-up with the independent test conducted on student's team.

5. Conclusion
The paper highlights the issue of that determines the Gap Analysis between 'WHAT' the customer wants and 'HOW' the software development company can achieve it. During the first stage a framework is developed using QFD and Messy Genetic Algorithm which selects the best possible combination of what the customer wants and how the company can achieve it. Secondly in the second stage it selects the independent team to analyze the Model Website developed by two team. The outcome of both the tests helped in determining the highest priority parameters of both the sets i.e. 'WHAT' and 'HOW'. In future we will be developing a framework which will use multi objective Messy Systems evolving to a more accurate result.

6. References
[1]. Hashem, M.M.A: 'Global Optimization Through a new class of Evolutionary Algorithms', PhD Dissertation, Saga University, Japan, 1999.
[2]. Norberto Eiji Nawa and Takeshi Furuhashi, 'Fuzzy system Parameters. Discovery by bacterial Evolutionary Algorithm', IEEE Transaction on Fuzzy system. Volume 7, No5, 1991.
[3]. Antonio Gonzalez and Francisco Hemera , 'Multi- stage Genetic fuzzy systems based on the Iterative Rule Learning Approach ', Math ware & Soft Computing 4(1997).
[4]. Microsoft Corporation, 'Usability in Software Design'. A while paper by Microsoft, October 2000.
[5]. David A and Gary B. Lamont,'Multi Objective Optimization with Messy Genetic Algorithm', S.Ac, 2000 Italy.
[6]. Yonghua Zhou Yuliu Chen, 'The QFD-based Decision-making Approach for Strategic BPR' Beijing 100084, P. R. China.
[7]. TIAN Na, CHE A-da, 'Goal Programming in Quality Function Deployment Using Genetic Algorithm', International Conference on Management Science & Engineering (14th) August 20-22, 2007.
[8]. K.Y. Chan1, T.S. Dillon1, C.K. Kwong2 and S.H. Ling,' Using Genetic Programming for Developing Relationship between Engineering Characteristics and Customer Requirements in New Products' 1Digital Ecosystems and Business Intelligence Institute, Curtin University of Technology, Perth, Australia.
[9]. Qi Wang, Jie Zhu, Bo Yu, 'Feature Selection and Clustering in Software Quality Prediction', Evaluation and Assessment in Software Engineering 2007.
[10]. Linda Murphy, Hoda S. Abdel-Aty-Zohd , M.Hashem-Sherif, 'A Genetic Algorithm Tracking Model
For Product Deployment in Telecom Services' , 0-7803-9197-7/05/?? 2005 IEEE.
[11]. Sanjay Kumar Dubey, Soumi Ghosh, Prof. (Dr.) Ajay Rana ,'Comparison of Software Quality Models: An Analytical Approach', ISSN 2250-2459, Volume 2, Issue 2, February 2012.
[12]. Yi. Liu, Taghi M.Khoshgoftaar, 'Genetic Programming Model for Software Quality Classification', 6th IEEE International Symposium on High Assurance Systems Engineering (HASE'01) 2001.
[13]. S. Keshavarz and Reza Javidan, 'Software Quality Control Based on Genetic Algorithm', Vol. 3, No. 4, August 2011.
[14]. Salah Bouktif, Bal??azs K??egl, Houari Sahraoui, 'Combining Software Quality Predictive Models: An Evolutionary Approach', Dept. of Computer Science and Op. Res., University of Montreal C.P. 6128 Succ. Centre-Ville, Canada.
[15]. Hillol Kargupta, 'The Gene Expression Messy Genetic Algorithm', published in IEEE conference on Evolutionary Computation, Nagoya, Japan, 1996.
[16}. Kohei Arai, 'Clustering Method Based on Messy Genetic Algorithm: GA for Remote Sensing Satellite Image Classifications', International Journal of Advanced Research in Artificial Intelligence, Vol. 1, No. 8, 2012.

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