Development and application of new quality model for software projects.
Due to the rapid development of the software industry today, software companies are now facing a highly competitive market. To succeed, companies have to make efficient project plans to reduce the cost of software construction . However, in medium to large scale projects, the problem of project planning is very complex and challenging [2, 3]. Despite numerous efforts in the past decade at defining and measuring software quality, the management of software quality remains in its infancy and the quality concept is still unutilized. The objectives of software testing are to qualify a software program's quality by measuring its attributes and capabilities against expectations and applicable standards. Software testing also provides valuable information to the software development effort. Throughout the history of software development, there have been many definitions and advances in software testing. Figure 1 graphically illustrates these evolutions .
The life cycle development or waterfall approach breaks the development cycle down into discrete phases, each with a rigid sequential beginning and end. Each phase is fully completed before the next is started. Once a phase is completed, in theory during development, one never goes back to change it . A software product is successful, if the requirements are fulfilled and no budget or deadline overflows occur . So, the Software size contains important information for project planning. The method estimates the software size by counting the "function points" of the system. This method, developed by , is widely used in the USA and Europe.
Software quality assurance activities play an important role in producing high quality software. The majority of quality assurance research is focused on defect prediction models that identify defect-prone modules [7-11]. Although such models can be useful in some contexts, they also have their drawbacks. The literature makes two things clear about defect prediction. First, no single prediction technique dominates  and, second, making sense of the many prediction results is hampered by the use of different data sets, data preprocessing, validation schemes, and performance statistics [12-15]. These differences are compounded by the lack of any agreed reporting procedure or even the need to share code and algorithms .
The COQUALMO model can play an important role in facilitating the balance of cost/schedule and quality . Recognizing this important association, COQUALMO was created as an extension of the Constructive Cost Model (COCOMO), for predicting the number of residual defects in a software product. The COQUALMO model contains two submodels (the model shown in Figure 2):
(1) the defect introduction model,
(2) the defect removal model.
The defect introduction model uses a subset of COCOMO cost drivers and three internal baseline defect rates (requirements, design, code, and test baselines) to produce a prediction of defects, which will be introduced in each defect category during software development. The defect removal model uses the three defect removal profile levels, along with the prediction produced by the defect introduction model, to produce an estimate of the number of defects that will be removed from each category . However, there are also a number of studies that do not confirm these results, especially regarding residual defects.
The importance of finding residual defects and reducing them (defect-free) in software products need not be accentuated. To reduce the number of defects during the design and development stage, well known product development tools such as the failure mode and effects analysis (FMEA), failure mode effects and criticality analysis (FMECA), quality function deployment (QFD), and sneak analysis (SA) are employed . They provide the reader with a historical sketch of software testing, followed by a description of how to transform requirements to test cases, when there are well-defined or not so well-defined requirements.
This paper aims to overcome these issues and combine these models (waterfall and COQUALMO) to develop a suitable new model, namely, software testing defect corrective model (STDCM). The STDCM will serve as a better model for finding the number of defects and producing high quality software in the IT industry, without increasing the cost and schedule.
2. Limitations of COQUALMO
(i) The effort to fix the defect introduced and removed is not quantified directly by the model.
(ii) COQUALMO does not associate the time aspect with the defect removal and fixing process.
(iii) Defects are not given weights and classifications, in terms of the software artifact they originated from.
3. Motivation for Construction of Software Testing Defect Corrective Model
(1) Can the existing model (waterfall and COQUALMO) achieve the maximum quality?
(2) Is the prediction of errors necessary in the testing phase?
(3) What are the possible ways to achieve the maximum quality of the model without increasing the cost and time?
These questions were answered by collecting the relevant literature in the area of software quality and a detailed Integrated model was constructed, as shown in Figure 3.
4. Related Works
This study was aimed at improving the quality and testing the process in the software projects, by reducing and finding the residual defects in software application projects. More numbers of papers relevant to the area are listed in below.
Phan et al. (1995) used IBM's OS/400 R.1 project to address key quality management and control issues in large development projects. The major task is improving the software quality during and after development. During the development of OS/400 R.1 at IBM corporation, thousands of programmers were involved in writing and refining millions of lines of code. Such an effort would fail without good software quality management. Hence, software developers cannot make good software quality products .
Cai (1998) applied a new static model for estimating the number of remaining defects and used a set of real data to test the new model. The new model resembles the Mills model in a particular case and is attractive in its applicability to a broader scope of circumstances. A practical example shows that the new model can offer good estimates for a number of software defects. It is also applicable to statistical problems other than software reliability modeling. They have not given a systematic review and hence cannot be applied for estimating the number of remaining defects .
Biffl et al. (2003) compared and investigated the performance of objective and subjective defect content estimation techniques. For the validation of these techniques they conducted a controlled experiment with 31 inspection teams, each of which consisted of 4-6 persons. They reported the data from an experiment with number of software engineering defects in a requirement document. They used the relative error, a confidence level, and the correctness for deciding reinspection as the main evaluation criterion, but they did not provide the major defects in the requirement document .
Chun (2006) applied a new method (capture-recapture model) that estimates the number of undetected errors in complex software design documents. This idea used the correlation matrix of multiple inspectors to formulate the estimation problem as a goal program. The capture-recapture model initially used by biologists to estimate the size of wildlife population has been widely used to estimate the number of software design errors. It shows that undetected errors are present and this leads to Software fault or failure .
Ravishanker et al. (2008) applied the nonhomogeneous Poisson process model and the multivariate model, that applies Markov switching to characterize the software defect discovery process. However, the process remains complex and hence there is an increase in failure rate 23].
Jacobs et al. (2007) studied the defect injection (DI) and defect detection (DD) influencing factors and their grouping, resulting in their use in development projects. To decrease the number of injected defects in a development project, the DI factors could be used as areas of attention, while the quality of documentation is poor, leading to a lack of product quality .
Quah et al. (2009) studied defect tracking as a proxy method to predict software readiness. They developed the defect predictive model, divided into three parts: (1) prediction model for presenting the logic tire, (2) prediction model for the business tier, and (3) prediction model for the data access tier. Evaluating the software readiness is very complex .
Chulani (1999) applied the COCQUALMO model to predict the defect density of the software under development, where defects conceptually flow into a holding tank through various defect introduction pipes and are removed through various defect removal pipes. In this model, it is difficult to increase the quality without increasing the cost and time. They inject the defects and remove them, but it involves more computation time, cost, and man power to predict the residual defects.
Westland (2004) analysed that the short software development life cycle appears to be in favor of higher rates of detection, but for some reasonable development cycle, most of the errors will be found incorrected. Short life cycles are likely to force constrained software development cycles and are likely to exacerbate the risk from postrelease defects. Defining uncorrected defects becomes exponentially costlier in each phase .
Turakhia et al. (2006) used statistical testing to isolate the embedded outlier population, test conditions, and test application support for the statistical testing framework and the data modeling for identifying the outliers. The identification of outliers that correlate to latent defects critically depends on the choice of the test response and the statistical model's effectiveness in estimating the healthy-die response, but it provides low efficiency and less reliability, and the cost is very high .
Xiaode et al. (2009) studied the quality prediction model and found that the number of faults is negatively correlated with the workload fluctuation, which indicates that the quality is decreasing due to the heavy workload. Due to the problems that occurred in the testing pahse for overheads, there is a decrease in the quality of the product .
Catal (2011) studied the software engineering discipline that contains several prediction approaches such as test effort prediction, correction cost prediction, fault prediction, reusability prediction, security prediction, effort prediction, and quality prediction. They investigated 90 software fault prediction papers published between 1990 and 2009. They gave a road map for research scholars in the area of software fault prediction .
However, the related works summarized are found to be more relevant for defect-free software products. "Estimating the remaining defects" and "predicting the residual defects" were found to be more suitable for the construction of STDCM. "Software estimation models" COQUALMO can play an important role in facilitating the balance of cost/schedule and quality. COQUALMO was created as an extension of the constructive cost model (COCOMO). The COQUALMO model contains two submodels.
The following conclusions can be drawn from the review of the literature.
(i) None of the alternatives is better than the others in all aspects.
(ii) The waterfall and COQUALMO models do not emphasize more the correctiveness of software testing.
(iii) The strengths and weaknesses of the other techniques are complementary.
5. Model Development Methodology
The STDCM has been developed based on two important traditional models, namely, waterfall and COQUALMO. The integration of these models was developed in a stagewise manner. The validation process of the model was built in a stagewise fashion. The framework of STDCM is shown in Figure 3. The following are the steps for the development of STDCM methodology.
Step 1 (requirement analysis). The customer requirements of the project are collected in the requirements phase of STDCM. The requirements are analyzed using soft QFD (SQFD), in which the house of quality (HoQ) matrix was used, to validate the requirements of the given project.
Step 2 (design). The input of this phase is SRS and the output is DD. This phase is validated using the design document process.
Step 3 (coding). The lines of code are estimated using COCOMO/FPA. The code review process is used and it can be validated using statistical tools.
Step 4 (testing). The inputs for this phase are the test cases and SWFMEA.
6. Application and Implementation of STDCM
A case study was conducted in a leading software company in India. The company was CMM level-5 certified in developing software projects for banking applications. It uses the offline review process for software quality improvement.
This project is basically used for credit card transaction (CCT) in the banking sector. It helps in the following cases:
(i) within project team communication validation,
(ii) to address the credit card business problem,
(iii) within project team URL (uniform resource locator) validation,
(iv) centralized location for all URL and phone numbers,
(v) transaction services (TS) team with online screens to monitor and validate,
(vi) a monthly scan process that allows for a continual review and update of the phone number and URLs.
The STDCM model was applied in this credit card transaction to improve the quality of project delivery. The following are the implementation details.
(i) The house of quality (HoQ) matrix was used to validate the correlations and relationships of customer requirements and functional requirements (see Figure 5).
(ii) The absolute and relative importance of TRs are computed using the customer importance of CIs and the relationship ratings of the project (see Figure 5).
(iii) The STDCM model conducted the code review process for the code developer and external code reviewer to reveal the code defects and resolve it (see Tables 1 and 2).
(iv) The STDCM model includes occurrence rating, severity rating, and detecting ability rating table for finding the defects criteria (see Table 3).
(v) The STDCM models were implemented corrective actions in the developing software projects with the help of SWFMEA (see Table 4 and Figure 3).
(vi) The SWFMEA is validated using the single paired f-test. The statistical paired t-test shows that there is significant improvement in the quality, in terms of the revised RPN values (Figure 4), due to the implementation of the SWFMEA (see Tables 5, 6, 7, and 8).
(vii) A Paired correlation report highly correlated in the projects is 0.77. So there is a significant difference between RPN1 and RPN2. It increases the confidence level up to 98% (see Tables 6-8).
6.1. Building the Software-HOQ (Table 1)
6.1.1. WHATs. Customer requirements are structured using affinity and tree diagrams and incorporated as WHATs of the software-HOQ. The WHATs are ranked in the order of importance using the analytic hierarchy process and comparison by pairs , which are shown in Figure 5. The priority of customer importance displayed in the HOQ next to each customer's voice has been obtained from the QFD team in a scale of 1 to 10. This gives the customer importance or priority rating for the WHATs of the software-HOQ. Number one implies low importance of priority and ten implies high importance of priority.
6.1.2. HOWs. The HOWs usually represent the product features, design requirements, product characteristics, product criteria, substitutes of quality characteristics, and technical requirements. The HOWs represent the means by which a company responds to what the user wants and needs. These technical requirements are listed along the top of the software house of quality. Each technical requirement may affect one or more of the customer voices. Using the voice of the engineer table (Figure 5), the technical requirements were identified as similar to the customer requirements and are represented as HOWs in the software-HOQ.
The entire soft QFD process has been carried out by a QFD team with members from all departments (development, quality management, marketing, sales, service, etc.) and extended in several team meetings by the representatives of the client company.
6.2. Absolute and Relative Importance
6.2.1. Absolute Importance. In Soft QFD applications, a cell (i, j) in the relationship matrix of HOQ, that is, ith row and jth column of HOQ, is assigned 9, 3, and 1 to represent a strong, medium, or weak relationship, respectively, between the ith customer requirement (CR) and the jth Technical requirement (TR). The absolute and relative importance of TRs are computed using the customer importance of CIs and the relationship ratings, that is, 9-3-1. For each TR, the absolute importance rating is computed as
[AI.sub.i] = [m.summation over (i=1)] [CI.sub.i] x [R.sub.ij], (1)
where [AI.sub.j] is the absolute importance of [TR.sub.j], j = 1,...,n. [CI.sub.i] is the customer importance, that is, importance rating of [CR.sub.i], i = 1,..., m. is the relationship rating representing the strength of the relationship between CR; and [TR.sub.j]. (m) Relative importance: the absolute importance rating can then be transformed into the relative importance rating, [RI.sub.j], as
[RI.sub.j] = [AI.sub.j]/n, (2)
[n.summation over (k=1)] [AI.sub.k]. (3)
The larger the [RI.sub.j] is, the more important the [TR.sub.j] is. Thus, without consideration of any other constraints such as cost and time, TRs should be incorporated into the product of interest in the order of their relative importance rating to achieve more customer satisfaction.
The absolute importance [AI.sub.j] for each technical requirement is calculated using (1). Referring to Table 1, the technical requirement "Skillset" has a strong relationship with the customer requirement "Import valid URLs/Phone Numbers." Thus the column weight for the first column is computed as (9 x 9) + (8 x 9) + (8 x 9) + (7 x 9) + (7 x 9) + (5 x 9) + (4 x 9) + (4 x 9) + (4 x 9) + (5 x 9) = 549. The column weights are used to identify the technical requirements for quality improvement. The relative importance ([RI.sub.j]) for each technical requirement is calculated using (2).
6.3. Test of Hypotheses. Hypothesis testing helps to decide the basis of sample data, whether a hypothesis about the population is likely to be true or false. Statisticians have developed several tests of hypotheses (also known as tests of significance) for the purpose of testing of hypotheses which can be classified as (a) parametric tests or standard tests of hypotheses and (b) nonparametric tests or distribution-free test of hypotheses. Parametric tests usually assume certain properties of the parent population from which we draw samples. So, we have chosen parametric tests for testing our project samples .
6.3.1. Important Parametric Tests. The important parametric tests are (1) z-test, (2) t-test, (3) [X.sup.2]-test, and (4) F-test. All these tests are based on the assumption of normality; that is, the source of data is considered to be normally distributed. Our project sample data size is small; so, we have chosen the t-test. The relevant test statistic, f, is calculated from the sample data and then compared with its probable value based on t-distribution (to be read from the table that gives probable values of t for different levels of significance for different degrees of freedom) at a specified level of significance concerning the degrees of freedom for accepting or rejecting the null hypothesis .
6.3.2. Hypothesis Testing of Means. Hypothesis testing refers to the formal procedures used by statisticians to accept or reject statistical hypotheses.
In such a situation t-test is used and the test statistic t is worked out as follows under
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (4)
where [bar.X] = mean of the sample, [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] = hypothesized mean for population, [[sigma].sub.s] = standard deviation of sample, and n = number of items in a sample.
This paper discusses the development of a new model, namely, the STDCM, which uses the estimation of the COQUALMO and waterfall models. Its construction and applicability have been discussed. The limitations of other models are highlighted, in particular, the COQUALMO and waterfall. The framework shows the application of the SQFD and SWFMEA in STDCM and the size estimation computation of COCOMO/FPA. The STDCM will serve as a better model for finding the number of defects and producing high quality software in the software industry. The model was demonstrated using banking applications and specializes in developing credit cards. Moreover, the model was successfully validated, using the statistical inference.
Conflict of Interests
The authors declare that there is no conflict of interests regarding the publication of this paper.
 N. Nan and D. E. Harter, "Impact of budget and schedule pressure on software development cycle time and effort," IEEE Transactions on Software Engineering, vol. 35, no. 5, pp. 624-637, 2009.
 J. Duggan, J. Byrne, and G. J. Lyons, "A task allocation optimizer for software construction," IEEE Software, vol. 21, no. 3, pp. 76-82, 2004.
 W.-N. Chen and J. Zhang, "Ant colony optimization for software project scheduling and staffing with an event-based scheduler," IEEE Transactions on Software Engineering, vol. 39, no. 1, pp. 1-17, 2013.
 W. D. Barnett and M. K. Raja, "Application of QFD to the Software development process," International Journal of Quality and Reliability Management, vol. 12, no. 6, pp. 24-42, 1995.
 L. D. Paulson, "Adapting methodologies for doing software right," IT Professional, vol. 3, no. 4, pp. 13-15, 2001.
 A. J. Albrecht and J. E. Gaffney Jr., "Software function, source lines of code, and development effort prediction: a software science validation," IEEE Transactions on Software Engineering, vol. SE-9, no. 6, pp. 639-648, 1983.
 T. Gyimothy, R. Ferenc, and I. Siket, "Empirical validation of object-oriented metrics on open source software for fault prediction," IEEE Transactions on Software Engineering, vol. 31, no. 10, pp. 897-910, 2005.
 A. E. Hassan, "Predicting faults using the complexity of code changes," in Proceedings of the 31st International Conference on Software Engineering (ICSE '09), pp. 78-88, Vancouver, Canada, May 2009.
 P. L. Li, J. Herbsleb, M. Shaw, and B. Robinson, "Experiences and results from initiating field defect prediction and product test prioritization efforts at ABB Inc," in Proceedings of the 28th International Conference on Software Engineering (ICSE '06), pp. 413-422, May 2006.
 J. C. Munson and T. M. Khoshgoftaar, "The detection of fault-prone programs," IEEE Transactions on Software Engineering, vol. 18, no. 5, pp. 423-433, 1992.
 Y. Kamei, E. Shihab, B. Adams et al., "A large-scale empirical study of just-in-time quality assurance," IEEE Transactions on Software Engineering, vol. 39, no. 6, pp. 757-773, 2013.
 S. Lessmann, B. Baesens, C. Mues, and S. Pietsch, "Benchmarking classification models for software defect prediction: a proposed framework and novel findings," IEEE Transactions on Software Engineering, vol. 34, no. 4, pp. 485-496, 2008.
 I. Myrtveit, E. Stensrud, and M. Shepperd, "Reliability and validity in comparative studies software prediction models," IEEE Transactions on Software Engineering, vol. 31, no. 5, pp. 380-391, 2005.
 T. Raeder, T. R. Hoens, and N. V. Chawla, "Consequences of variability in classifier performance estimates," in Proceedings of the IEEE 10th International Conference on Data Mining, pp. 421-430, December 2010.
 Q. Song, Z. Jia, M. Shepperd, S. Ying, and J. Liu, "A general software defect-proneness prediction framework," IEEE Transactions on Software Engineering, vol. 37, no. 3, pp. 356-370, 2011.
 D. C. Ince, L. Hatton, and J. Graham-Cumming, "The case for open computer programs," Nature, vol. 482, no. 7386, pp. 485-488, 2012.
 S. Chulani, "Constructive Quality Modeling for Defect Density Prediction: COQUALMO," IBM Research, Center for Software Engineering ,1,408-927-1767.
 R. Dillibabu and K. Krishnaiah, "Application of failure mode and effects analysis to software code reviews--a case study," Software Quality Professional, vol. 8, no. 2, pp. 30-41, 2006.
 D. D. Phan, J. F. George, and D. R. Vogel, "Managing software quality in a very large development project," Information and Management, vol. 29, no. 5, pp. 277-283, 1995.
 K.-Y. Cai, "On estimating the number of defects remaining in software," Journal of Systems and Software, vol. 40, no. 2, pp. 93-114, 1998.
 S. Biffl, "Evaluating defect estimation models with major defects," Journal of Systems and Software, vol. 65, no. 1, pp. 13-29, 2003.
 Y. H. Chun, "Estimating the number of undetected software errors via the correlated capture-recapture model," European Journal of Operational Research, vol. 175, no. 2, pp. 1180-1192, 2006.
 N. Ravishanker, Z. Liu, and B. K. Ray, "NHPP models with Markov switching for software reliability," Computational Statistics & Data Analysis, vol. 52, no. 8, pp. 3988-3999, 2008.
 J. Jacobs, J. van Moll, R. Kusters, J. Trienekens, and A. Brombacher, "Identification of factors that influence defect injection and detection in development of software intensive products," Information and Software Technology, vol. 49, no. 7, pp. 774-789, 2007
 T.-S. Quah, "Estimating software readiness using predictive models," Journal of Information Science, vol. 179, no. 4, pp. 430-445, 2009.
 J. C. Westland, "The cost behavior of software defects," Decision Support Systems, vol. 37, no. 2, pp. 229-238, 2004.
 R. P. Turakhia, W. R. Daasch, J. Lurkins, and B. Benware, "Changing test and data modeling requirements for screening latent defects as statistical outliers," IEEE Design and Test of Computers, vol. 23, no. 2, pp. 100-109, 2006.
 Z. Xiaode, L. Yun, and L. Hong, "Study on the quality prediction model of software development," in Proceedings of the International Conference on E-Business and Information System Security (EBISS '09), pp. 1-4, May2009.
 C. Catal, "Software fault prediction: a literature review and current trends," Expert Systems with Applications, vol. 38, no. 4, pp. 4626-4636, 2011.
 J. D. Musa, A. Iannino, and K. Okumoto, Software Reliability, McGraw-Hill, New York, NY, USA, professional edition, 1990.
 C. R. Kothari, Research Methodology Methods and Techniques, New Age International, New Delhi, India, 2nd edition, 2004.
K. Karnavel and R. Dillibabu
Department of Industrial Engineering, Anna University, Chennai 25, India
Correspondence should be addressed to K. Karnavel; firstname.lastname@example.org
Received 27 May 2014; Revised 16 September 2014; Accepted 10 October 2014; Published 16 November 2014
Academic Editor: Jung-Hua Lo
TABLE 1: Code review form. Project name: Credit Card Transaction Code Review Form Date of walkthrough: 10/11/2011 Sl.no. Issue Issue Open date Status Severity raised by owner (open S, H, M, L /closed) i XXX YYY 10/11/2011 Closed L 2 XXX YYY 10/11/2011 Closed L 3 XXX YYY 10/11/2011 Closed L Project name: Credit Card Transaction Code Review Form Date of walkthrough: 10/11/2011 Sl.no. Code Impacted language module Issue description i PEGA CCT delete While using embedded pages screen in the activities like CCT DeleteRow, instead of using the following when condition ContactsPage.pxResults (local.i) .pyRowS electe d=="true" 2 PEGA CCT update When step page is pyWorkPage screen instead of using the following when condition pyWorkPage.URL_NUMBER ==pyWorkPage.URLT4UMBER _ NEW&&pyWorkPage.Choice CCTElomeScreen=="Update" 3 PEGA CCT review Steps with many labels all screens Project name: Credit Card Transaction Code Review Form Date of walkthrough: 10/11/2011 Sl.no. Was module unit tested Resolution Closed (yes/no) date i Yes .pyRowSelected 10/12/2012 =="true" 2 Yes We can prefer using 10/12/2012 .URL_NUMBER==.URL_ NUMBER_NEW&&. Choice CCTHomeScreen==<<Update" 3 Yes Step description can 10/12/2012 be given to avoid confusion TABLE 2: Requirements and application issue count reports for the CCT project. Application/project Credit card transaction (CCT) Requirements (1) Improve valid URLs /phone numbers in the database (2) Review new URLs/phone numbers (3) Review all URLs/phone numbers (4) Export the history/entire data (5) Export not-working aging report and productivity report (6) Update URLs/phone numbers (7) Update status (8) Delete teams impacted Rules/LOC 776 Estimation splitup (hrs) 780 + 46 Total estimation (hrs) 826 Code issue count 12 Data issue count 22 Application issue count 2 TABLE 3: Occurrence rating process SWFMEA. Occurrence rating Probability Rating Criteria of failure Very frequently 10 Shows greater than 80% of defects attributable to subprocess failure Frequently 8 Shows greater than 40% of defects & less than 80% of defects attributable to subprocess failure Sometimes 4 Shows greater than 10% of defects & less than 40% of defects attributable to subprocess failure Rare 1 Shows less than 10% of defects to subprocess failure Severity rating Effect Rating Criteria Very high 10 Rework is greater than 8 ph High 7 Rework is greater than 4 ph & <8ph Moderate 5 Rework is greater than 2 ph & <4 ph Low 3 Rework is greater than 1 ph & <2 ph Very low 1 Rework is greater than 1 ph Detectability rating Detection Rating Criteria Difficult 10 No defined methods for identifying the process error; only the o/p prdt analysis leads to detectability Moderate 5 Can be identified in the exit phase of the process or subsequent process entry check Easy 1 Process has built in checks for identifying subprocess failure TABLE 4: Complete processes of software failure mode and effect analysis. Function Subprocess Mode of failure Effect of /process failure Coding Planning Inadequate skills Unable to /knowledge about access the the process application Coding Planning Inadequate time Delivery not Requirement change in time Inappropriate skilled resources for DEV Testing & review objective is not clear Inappropriate skilled resources for testing & review Coding Development Not reusing the File size is existing rules huge to move Coding Testing Invalid data Defects raised Testing objective is not clear Inappropriate skilled resources for testing Coding Review Inappropriate skilled Defects resources for review raised Inadequate time Improper details about code review Function Subprocess Mode of failure Cause of /process failure Coding Planning Inadequate skills Request has /knowledge about not been the process raised to get access Coding Planning Inadequate time Incorrect Requirement change estimated Inappropriate hours skilled resources for DEV Testing & review objective is not clear Inappropriate skilled resources for testing & review Coding Development Not reusing the Increase in existing rules the count of rules Coding Testing Invalid data Improper data Testing objective handled is not clear while testing Inappropriate skilled resources for testing Coding Review Inappropriate skilled Reviewed resources for review unnecessary Inadequate time codes Improper details about code review Function Subprocess Mode of failure Current /process controls Coding Planning Inadequate skills This should be /knowledge about added in the process implementation checklist Coding Planning Inadequate time Estimation should Requirement change be done by Inappropriate skilled skilled resources resources for DEV Testing & review objective is not clear Inappropriate skilled resources for testing & review Coding Development Not reusing the Code review existing rules should be done to check the logic/rules handled Coding Testing Invalid data Proper unit test Testing objective scripts should is not clear be provided Inappropriate skilled resources for testing Coding Review Inappropriate skilled Should give the resources for review details about Inadequate time the code to be Improper details reviewed about code review CCT Current status Function Subprocess Mode of failure /process OCC SEV Coding Planning Inadequate skills 4 10 /knowledge about the process Coding Planning Inadequate time 4 10 Requirement change 8 10 Inappropriate 4 10 skilled resources for DEV Testing & review 1 5 objective is not clear Inappropriate 4 3 skilled resources for testing & review Coding Development Not reusing the 4 5 existing rules Coding Testing Invalid data 8 10 Testing objective 4 10 is not clear Inappropriate 4 10 skilled resources for testing Coding Review Inappropriate skilled 4 3 resources for review Inadequate time 1 3 Improper details 8 10 about code review CCT Current status Function Subprocess Mode of failure /process DET RPN1 Coding Planning Inadequate skills 1 40 /knowledge about the process Coding Planning Inadequate time 5 200 Requirement change 10 800 Inappropriate 5 200 skilled resources for DEV Testing & review 1 5 objective is not clear Inappropriate 5 60 skilled resources for testing & review Coding Development Not reusing the 1 20 existing rules Coding Testing Invalid data 1 80 Testing objective 5 200 is not clear Inappropriate 10 400 skilled resources for testing Coding Review Inappropriate skilled 1 12 resources for review Inadequate time 1 3 Improper details 1 80 about code review Function Subprocess Mode of failure Recommended /process corrective actions Coding Planning Inadequate skills Assigning SPOC /knowledge about to get access the process Coding Planning Inadequate time Project owner Requirement change /lead has to Inappropriate review the skilled resources estimation for DEV based on Testing & review requirements objective is given not clear Inappropriate skilled resources for testing & review Coding Development Not reusing the Proper training existing rules and development Coding Testing Invalid data Proper training Testing objective about the is not clear applicatior Inappropriate skilled resources for testing Coding Review Inappropriate skilled Sharing the code resources for review details with Inadequate time required rule Improper details set & rule set about code review version Function Subprocess Mode of failure Action by /process Coding Planning Inadequate skills Project /knowledge about manager the process (PM) Coding Planning Inadequate time Project Requirement change manager Inappropriate (PM) skilled resources for DEV Testing & review objective is not clear Inappropriate skilled resources for testing & review Coding Development Not reusing the Project existing rules manager (PM) Coding Testing Invalid data Project Testing objective manager is not clear (PM) Inappropriate skilled resources for testing Coding Review Inappropriate skilled Project resources for review manager Inadequate time (PM) Improper details about code review Function Subprocess Mode of failure Action taken /process Coding Planning Inadequate skills Knowledge /knowledge about transition the process about process Coding Planning Inadequate time To follow Requirement change estimation Inappropriate model with skilled resources proper training for DEV & knowledge Testing & review objective is not clear Inappropriate skilled resources for testing & review Coding Development Not reusing the Review by existing rules skilled resources Coding Testing Invalid data Valid unit test Testing objective script template is not clear is followed Inappropriate with valid skilled resources values & for testing validations Coding Review Inappropriate skilled To follow IQ A, resources for review EQA, and Inadequate time FI process Improper details before about code review implementation Revised status Function Subprocess Mode of failure /process OCC SEV Coding Planning Inadequate skills 1 10 /knowledge about the process Coding Planning Inadequate time 1 10 Requirement change 1 10 Inappropriate 1 10 skilled resources for DEV Testing & review 1 3 objective is not clear Inappropriate 1 1 skilled resources for testing & review Coding Development Not reusing the 1 5 existing rules Coding Testing Invalid data 4 10 Testing objective 1 10 is not clear Inappropriate skilled resources 1 10 for testing Coding Review Inappropriate skilled 1 3 resources for review Inadequate time 1 3 Improper details 1 10 about code review Revised status Function Subprocess Mode of failure /process DET RPN2 Coding Planning Inadequate skills 1 10 /knowledge about the process Coding Planning Inadequate time 1 10 Requirement change 5 50 Inappropriate 1 10 skilled resources for DEV Testing & review 1 3 objective is not clear Inappropriate 1 1 skilled resources for testing & review Coding Development Not reusing the 1 5 existing rules Coding Testing Invalid data 1 40 Testing objective 1 10 is not clear Inappropriate skilled resources 5 50 for testing Coding Review Inappropriate skilled 1 3 resources for review Inadequate time 1 3 Improper details 1 10 about code review TABLE 5: SWFMEA validation report (RPN1 TO RPN2). CCT [H.sub.1]: not significant in RPN1 & RPN2 [H.sub.0]: significant in N = 13, SQRT(N) RPN1 & RPN2 = 3.60551 Mode of failure RPN1 (x - [mu]) [(x - [mu]).sup.2] Inadequate skills 40 -121.538 14771.6 /knowledge about the process Inadequate time 200 38.46154 1479.29 Requirement change 800 638.4615 407633.1 Inappropriate skilled 200 38.46154 1479.29 resources for DEV Testing & review objective 5 -156.538 24504.29 is not clear Inappropriate skilled 60 -101.538 10310.06 resources for testing & review Not reusing the existing 20 -141.538 20033.14 rules Invalid data 80 -81.5385 6648.521 Testing objective is 200 38.46154 1479.29 not clear Inappropriate skilled 400 238.4615 56863.91 resources for testing Inappropriate skilled 12 -149.538 22361.75 resources for review Inadequate time 3 -158.538 25134.44 Improper details about 80 -81.5385 6648.521 code review Total 2100 599347.2 Mean ([mu]) 161.5385 49945.6 Standard deviation 223.4851 ([sigma]) Variance 49945.6 ([[sigma].sup.2]) Standard error mean 61.98433 ([sigma]/SQRT(N)) CCT [H.sub.1]: not significant in RPN1 & RPN2 [H.sub.0]: significant in N = 13, SQRT(N) RPN1 & RPN2 = 3.60551 Mode of failure RPN2 (x - [mu]) [(x - [mu]).sup.2] Inadequate skills 10 -5.76923 33.28402 /knowledge about the process Inadequate time 10 -5.76923 33.28402 Requirement change 50 34.23077 1171.746 Inappropriate skilled 10 -5.76923 33.28402 resources for DEV Testing & review objective 3 -12.7692 163.0533 is not clear Inappropriate skilled 1 -14.7692 218.1302 resources for testing & review Not reusing the existing 5 -10.7692 115.9763 rules Invalid data 40 24.23077 587.1302 Testing objective is 10 -5.76923 33.28402 not clear Inappropriate skilled 50 34.23077 1171.746 resources for testing Inappropriate skilled 3 -12.7692 163.0533 resources for review Inadequate time 3 -12.7692 163.0533 Improper details about 10 -5.76923 33.28402 code review Total 205 3920.31 Mean ([mu]) 15.76923 326.6925 Standard deviation 18.07464 ([sigma]) Variance 326.6925 ([[sigma].sup.2]) Standard error mean 5.01306 ([sigma]/SQRT(N)) [H.sub.0]: there is no significant difference between RPN1 and RPN2. [H.sub.1]: there is a significant difference between RPN1 and RPN2. TABLE 6: Paired correlations report. To find correlations (2 tailed) Correlation (r) 0.773723 P value 0.027661 <0.05 (so reject [H.sub.0]) 0.77: it is highly correlated. So there is a significant relationship between RPN1 and RPN2. TABLE 7: Paired samples test. Paired samples test (2 tailed) Paired difference mean 145.7692 Paired difference standard 205.4105 deviation ([sigma]) Paired difference standard 56.97127 error mean ([sigma]/SQRT(N)) [[sigma].sup.2]/(N - 1) 4162.134 Total [[sigma].sup.2]/(N - 1) 4189.358 SQRT([[sigma].sup.2]/(N - 1)) 64.72525 t = Paired Difference 2.252123 Mean/SQRT([[sigma].sup.2] /(N - 1)) Degrees of freedom (df) 12 t table value 1.78 <2.25 (so reject [H.sub.0]) TABLE 8: Confidence limits reports. To find confidence limits (2 tailed) t(0.025,12) 2.18 Lower level confidence limit 21.57186 Upper level confidence limit 269.9666 Confidence level: 98% [+ or -] 5% accuracy.
|Printer friendly Cite/link Email Feedback|
|Title Annotation:||Research Article|
|Author:||Karnavel, K.; Dillibabu, R.|
|Publication:||The Scientific World Journal|
|Date:||Jan 1, 2014|
|Previous Article:||Effect of multipath laser shock processing on microhardness, surface roughness, and wear resistance of 2024-T3 al alloy.|
|Next Article:||Hematology of wild caught Dubois's tree frog Polypedates teraiensis, Dubois, 1986 (anura: rhacophoridae).|