The Fuzzy Linguistic Preference Relation Approach Accounting Essay

Published: 2021-06-18 04:20:05
essay essay

Category: Accounting

Type of paper: Essay

This essay has been submitted by a student. This is not an example of the work written by our professional essay writers.

Hey! We can write a custom essay for you.

All possible types of assignments. Written by academics

GET MY ESSAY
Abstract. This study investigates and identifies the risk factors for software development projects from a literature review and expert interviews. Based on the identified assessment factors, a hierarchical structure of five risk dimensions and twenty-two risk factors is constructed, and the fuzzy linguistic preference relation (FLPR) is employed to assess the relative degree of impact and determine the priorities for these risk factors for two expert groups working in technology enterprises and software development companies. Among the identified dimensions, "Organization Function Risk" is considered the most important dimension influencing the software development project performance, with the others, in order, being "Developing Technology Risk", "Resources Integration Risk", "Personnel System Risk" and "System Requirement Risk".
Keywords: software development project, risk assessment, fuzzy linguistic preference relation
1. Introduction. With the rapid economic growth and innovation in the field of information technology (IT), software development and applications have become very important to an enterprises’ operations. Software development projects are carried out by highly skilled professionals, due to their complicated, professional, technical and other features. However, software development projects have a dismal track-record of cost and schedule overruns and quality and usability problems [1]. It has been pointed out that most software development projects use more resources than planned, take more time to be completed, and deliver less functionality and less quality than expected [2]. Those problems are caused because the software development process faces numerous uncertainties and risk factors. Therefore, an effective risk assessment model can not only be used to facilitate identifying and measuring critical risk factors, but also help to achieve a software development project’s goals.
Risk-based project management has become a popular issue in many types of practical projects and associated academic studies. There are numerous software development risks, so the proposed assessment procedure is designed using the multiple criteria decision making (MCDM) approach. Associated risk evaluation is often determined by experts subjectively, however, it is not always easy to obtain incisive judgments from the experts within the time limits. Therefore, to solve the problem of incorporating more assessment factors and working within time limitations for interviews with experts, this study applies the fuzzy linguistic preference relation (FLPR) proposed by Wang and Chen [3]. This method is based on the consistent fuzzy preference relations (CFPR) approach which was proposed by Herrera-Viedma et al. [4] to deal with the relative degree of impact of software development risk factors. The results can provide decision makers with the ability to propose risk management strategies in advance of implementing a software development project.
2. Risk Factors for a Software Development Project. When software development projects are performed, many difficult problems will be encountered regardless of whether the project is executed by oneself or by outsourcing. To reduce the project failure rate, various risk factors affecting project outcome should be identified by software engineering and information systems researchers. Ten risk items for software development projects were identified by Boehm [5], which include: personnel shortfalls, unrealistic schedules and budgets, developing the wrong functions and properties, developing the wrong user interface, adding more functionality/features, continual requirement changes, shortfalls in externally furnished components, shortfalls in externally performed tasks, real-time performance shortfalls, and straining of computer-science capabilities. A hierarchical structure model for evaluating software development risk was constructed via fuzzy set theory by Lee and Lin [6]. They proposed six risk attributes in order to evaluate the aggregative risk: personnel, system requirements, schedules and budgets, developing technology, external resource and performance. The fourteen risk items can be categorized into the six attributes.
The twelve risk categories identified by Jiang and Klein [7] include: technological acquisition, project size, lack of general team expertise, lack of team’s expertise with the task, lack of team’s development expertise, lack of user support, intensity of conflicts, extent of changes brought, resources insufficient, lack of clarity of role definitions, application complexity, and lack of user experience. In addition, Houston, Mackulak and Collofello [8] listed thirty software development risk factors and further studied the effects of six common and significant risk factors. They are creeping user requirements, lack of staff commitment, low morale, instability and lack of continuity in project staffing, inaccurate cost estimation, excessive schedule pressure, and lack of senior management commitment. Buyukozkan and Ruan [9] applied the Choquet integral aggregation approach to analyze the effects of importance and interactions among software development risks. They extracted software development risk factors by surveying a large amount of literature, and explored several types of risks including product engineering risks, development environment risks, and program constraint related risks such as the main risk dimensions. Factors associated with product engineering risks include requirements, design, code & unit tests, integration & testing, engineering specialists. Factors in development environment risks include development process, development system, management process, management methods, and work environment. Resource, contract and program interfaces are those factors that influencing the program constraint risks.
From the literature review, the software development risk factors were further screened and synthesized for this study. Twenty-two risk assessment factors divided into five dimensions were finalized. Five risk dimensions including the organization function, developing technology, personnel system, resource integration, and system requirement were identified. The hierarchical structure of the risk factors for a software development project is shown in Figure 1.
Figure . Hierarchical structure of risk factors for a software development project
3. Method for Assessing the Degree of Impact of Risk Factors. This study measures the impact degree of risk factors using an integrated measure of magnitude for unintentional events regarding project success. It is impractical to assume that the different project risk factors equally affect project success. To better manage project risk and increase the chance of project success, the impact degree of the risk factors on project success should be carefully evaluated and further used as fundamental information for the control, response and management of project risks. That is, the varying effects of project risk factors on project success provide valuable information needed to allocate software development project resources. This study also assesses the relative impact degree among software development risk factors for helping developers to draw up the risk management strategies. However, the largest possible number of evaluation criteria of a dimension is 5. If we apply the conventional analytic hierarchical process (AHP) proposed by Saaty [10], or fuzzy AHP proposed by Buckley [11], to design the questionnaire, we may encounter great difficulties and challenges in collecting information. In addition, the model also needs to consider the fuzziness of the judgments or opinions of selected experts when they answer the questionnaires. Thus, this study applies the fuzzy linguistic preference relation (FLPR) approach for constructing the decision matrices of pairwise comparisons. The FLPR approach was constructed by Wang and Chen [3] based on the consistent fuzzy preference relations (CFPR) proposed by Herrera-Viedma et al. [4]. The FLPR not only makes it easy for interviewers to use linguistic variables to present a set of criteria with the least amount of subjective judgment, it also avoids the necessity to check for consistency in the decision-making process. More importantly, it is more convenient to acquire the judgments of practitioners or experts of software development industry through a questionnaire. A brief introduction of the definitions and steps in the adopted FLPR method is given below.
Consistent Fuzzy Preference Relations (CFPR)
The fuzzy preference relation P on a set of evaluation criteria X is a fuzzy set of the product with a membership function. The preference relation is represented by the matrix, where . Herein, is interpreted as the degree of importance of criteria over . If, it means that and is equally important; indicates that is absolutely important to; shows that is more important than. In this case, the preference matrix, P, is usually assumed to be additive reciprocal, i.e., . When the reciprocal fuzzy preference relation to be consistent, verifies additive consistency, there exists a relationship equation such that [4]
. (1)
Equation (1) is very important because it can be used to construct a consistent fuzzy preference relation from the set of values . If a decision matrix with entries in the interval is outside the interval [0,1], it can be constructed by transforming the obtained values using a transformation function that preserves reciprocity and additive consistency. The transforming function is . In such a way, we can facilitate the expression by the evaluator of consistent preferences in the decision process.
Linguistic variables and fuzzy numbers
A linguistic variable is one whose values are words or sentences expressed in a natural or artificial language. Here, we use this kind of expression to compare the impact degree of two risk dimensions or factors for a software development project using linguistic terms: "absolutely (not) important," "very strongly (not) important," "essentially (not) important," "weakly (not) important," "equally important", with respect to a triangle fuzzy number (TFN) proposed by [12]. A TFN is a fuzzy number on if its membership function : [0,1] is equal to equation (2), following the definition in [11].
(2)
where l and u stand for the lower and upper bounds of the fuzzy number , respectively, and m stands for the median value. The TFN can be denoted by and Table 1 shows the corresponding TFN for each linguistic assessment variable.
Table 1. Fuzzy linguistic assessment variables
Linguistic variables
Triangle fuzzy numbers
Absolutely important (AB)
(0.90,1.00,1.00)
Very strongly important (VS)
(0.80,0.90,1.00)
Essentially important (ES)
(0.50,0.70,0.90)
Weakly important (WK)
(0.50,0.60,0.70)
Equally important (EQ)
(0.40,0.50,0.60)
Weakly not important (WN)
(0.30,0.40,0.50)
Essentially not important (EN)
(0.10,0.30,0.50)
Very strongly not important (VN)
(0.00,0.10,0.20)
Absolutely not important (AN)
(0.00,0.00,0.10)
Fuzzy linguistic preference relations (FLPR)
Given a set of criteria, associated with the fuzzy linguistic preference relations matrix based on consistent fuzzy preference relations and fuzzy linguistic assessment variables. If the matrix above complies with additive reciprocal consistency, then, the following statements must be equivalent:
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
Notably, if the values of the obtained matrix with elements in the interval (c>0) are not in the interval [0,1], the obtained fuzzy numbers would need to be transformed by way of a transformation function to preserve the reciprocity and additive consistency. The transformation function for left, median and upper values of TFN in every element of decision matrix are given in equations (12)-(14). Finally, we can obtain a consistent additive reciprocal decision matrix .
(12)
(13)
(14)
Determination of criteria weights
If there are m evaluators to participate in the judgments, this study uses the notion of the average value to integrate the fuzzy judgment values of m evaluators from the set of values, that is,
(15)
Here, we can establish an original synthetic fuzzy judgment matrix, and then use equations (3)-(14) to obtain the consistent synthetic fuzzy judgment matrix . In order to obtain the weights of each criterion, this study adopts the fuzzy geometric mean approach proposed by Buckley [11] as follows:
. (16)
The result of the fuzzy synthetic decision reached by each criterion is a fuzzy number, . Therefore, it is necessary that a defuzzification method for fuzzy numbers be used. Methods for such defuzzified weighting generally include the mean of maximal (MOM), center of area (COA), and α-cut methods [13] [14]. This study utilizes the COA method to find that the nonfuzzy value is a simple and practical method. The defuzzified weights of the fuzzy number can be found by the following equation:
. (17)
4. Case Study. Two companies were invited to answer the questionnaire related to the risk of a software development project. The first group is comprised of five experts with 5 to 15 years experience in the information management division from a medium scale technology enterprise. The second group is also comprised of five experts with 5 to 10 years experience in establishing enterprise resource planning (ERP) systems for a software development company. The individuals in the two groups were invited to provide responses to the questionnaire survey, and were asked to compare the relative degree of impact for the risk dimensions and factors identified in this study in pairwise sequential order. That is, they only needed to compare the relative impact degree of F1 to F2, F2 to F3, F3 to F4, F4 to F5, F11 to F12, F12 to F13, and so on. Tables 2 and 3 list the original evaluation results of this pairwise comparison for the risk dimensions given by the five experts of the two groups. Similarly, tables 4 and 5 display the original pairwise comparison matrix for the twenty-two risk factors given by the two groups. Since the preferences and experiences of these experts are different, we use the fuzzy linguistic assessment variables, as in table 1 and equations (3)-(15), to aggregate the experts’ subjective judgments toward the impact of the degree of risk dimensions and factors, yielding the synthesized triangular fuzzy numbers listed in tables 6, 7, 8 and 9 respectively. The ranking of the impact degree is determined by constructing a crisp value from the fuzzy number. Hence, defuzzification needs to be performed to arrange the fuzzy numbers for ranking. Table 10 lists the impact degree and ranking of risk dimensions and factors for software development projects obtained by using equations (16) and (17).
Table 2. Original evaluation results for the risk dimensions by the first group
E1
E2
E3
E4
E5
F1
VS
VS
EQ
WN
VN
F2
F2
VN
VS
EQ
AB
AB
F3
F3
VS
VN
WN
VS
VN
F4
F4
EQ
EN
VS
WK
EQ
F5
Table 3. Original evaluation results for the risk dimensions by the second group
E1
E2
E3
E4
E5
F1
ES
VS
VN
WK
EQ
F2
F2
EQ
WN
VS
VS
VS
F3
F3
ES
VS
EN
AN
WN
F4
F4
ES
WK
EQ
AB
WK
F5
Table 4. Original evaluation results for the risk factors by the first group
E1
E2
E3
E4
E5
F1:
F11
VS
AB
VS
EQ
AB
F12
F12
VN
VN
VN
ES
EN
F13
F 13
AB
WK
EQ
ES
WK
F14
F2:
F 21
VS
AB
VS
WK
ES
F22
F 22
VS
EQ
WK
WK
EN
F23
F 23
AN
VN
EN
EQ
VN
F24
F3:
F31
AN
VN
EQ
VN
AN
F32
F32
VS
VS
WK
VN
AB
F33
F33
AN
AN
VN
AN
VS
F34
F34
EQ
VS
AB
EQ
VN
F35
F4:
F41
EQ
EN
EQ
WN
EQ
F42
F42
VS
VN
VN
ES
VS
F43
F43
EQ
ES
WK
VS
WN
F44
F5:
F51
VS
AB
AB
VS
ES
F52
F52
EQ
VS
WN
VN
EN
F53
F53
EQ
VS
ES
ES
ES
F54
F54
VS
VN
WK
VS
EQ
F55
Table 5. Original evaluation results for the risk factors by the second group
E1
E2
E3
E4
E5
F1
F11
VS
ES
EQ
VS
WK
F12
F12
WN
EN
EQ
VN
EN
F13
F 13
ES
ES
EQ
VS
ES
F14
F2
F 21
WN
EN
EQ
AB
WN
F22
F 22
ES
ES
VS
VS
VN
F23
F 23
EN
VN
AN
AN
EN
F24
F3
F31
ES
VS
VN
AB
VS
F32
F32
ES
WK
VS
AB
VN
F33
F33
WN
EN
EQ
AN
EQ
F34
F34
ES
ES
EQ
AB
EQ
F35
F4
F41
WK
ES
EQ
AN
WK
F42
F42
ES
ES
ES
VS
EQ
F43
F43
ES
ES
EQ
VS
WK
F44
F5
F51
VS
VS
AB
AB
ES
F52
F52
EQ
EN
EQ
VS
WN
F53
F53
EQ
ES
EQ
VS
ES
F54
F54
EN
EN
VN
EN
WN
F55
Table 6. Aggregation decision matrix of the risk dimensions form the first group
F1
F2
F3
F4
F5
F1
(0.50,0.50,0.50)
(0.47,0.54,0.62)
(0.54,0.69,0.81)
(0.46,0.68,0.87)
(0.41,0.72,1.00)
F2
(0.38,0.46,0.53)
(0.50,0.50,0.50)
(0.57,0.65,0.69)
(0.49,0.63,0.75)
(0.44,0.68,0.88)
F3
(0.19,0.31,0.46)
(0.31,0.35,0.43)
(0.50,0.50,0.50)
(0.41,0.49,0.56)
(0.37,0.53,0.69)
F4
(0.13,0.32,0.54)
(0.25,0.37,0.51)
(0.44,0.51,0.59)
(0.50,0.50,0.50)
(0.46,0.54,0.63)
F5
(0.00,0.28,0.59)
(0.12,0.32,0.56)
(0.31,0.47,0.63)
(0.37,0.46,0.54)
(0.50,0.50,0.50)
Table 7. Aggregation decision matrix of the risk factors from the first group
F1
F11
F12
F13
F14
F11
(0.50,0.50,0.50)
(0.72,0.80,0.85)
(0.40,0.60,0.77)
(0.45,0.75,1.00)
F12
(0.15,0.20, 0.28)
(0.50,0.50,0.50)
(0.18,0.30,0.42)
(0.23,0.45,0.65)
F13
(0.23,0.40,0.60)
(0.58,0.70,0.82)
(0.50,0.50,0.50)
(0.55,0.65,0.73)
F14
(0.00,0.25,0.55)
(0.35,0.55,0.77)
(0.27,0.35,0.45)
(0.50,0.50,0.50)
F2
F21
F22
F23
F24
F21
(0.50,0.50,0.50)
(0.66,0.76,0.84)
(0.63,0.82,1.00)
(0.31,0.58,0.85)
F22
(0.16,0.24,0.34)
(0.50,0.50,0.50)
(0.47,0.56,0.66)
(0.15,0.32,0.52)
F23
(0.00,0.18,0.37)
(0.34,0.44,0.53)
(0.50,0.50,0.50)
(0.18,0.26,0.35)
F24
(0.15,0.42,0.69)
(0.48,0.68,0.85)
(0.65,0.74,0.82)
(0.50,0.50,0.50)
F3
F31
F32
F33
F34
F31
F31
(0.50,0.50,0.50)
(0.18,0.23,0.30)
(0.26,0.38,0.52)
(0.00,0.15,0.36)
(0.50,0.50,0.50)
F32
(0.70,0.77,0.82)
(0.50,0.50,0.50)
(0.58,0.65,0.71)
(0.32,0.42,0.56)
(0.70,0.77,0.82)
F33
(0.48,0.62,0.74)
(0.29,0.35,0.42)
(0.50,0.50,0.50)
(0.24,0.27,0.35)
(0.48,0.62,0.74)
F34
(0.64,0.85,1.00)
(0.44,0.58,0.68)
(0.65,0.73,0.76)
(0.50,0.50,0.50)
(0.64,0.85,1.00)
F35
(0.50,0.77,1.00)
(0.30,0.50,0.68)
(0.52,0.65,0.76)
(0.36,0.42,0.50)
(0.50,0.77,1.00)
F4
F41
F42
F43
F44
F41
(0.50,0.50,0.50)
(0.32,0.44,0.56)
(0.24,0.48,0.72)
(0.24,0.60,0.96)
F42
(0.44,0.56,0.68)
(0.50,0.50,0.50)
(0.42,0.54,0.66)
(0.42,0.66,0.90)
F43
(0.28,0.52,0.76)
(0.34,0.46,0.58)
(0.50,0.50,0.50)
(0.50,0.62,0.74)
F44
(0.04,0.40,0.76)
(0.10,0.34,0.58)
(0.26,0.38,0.50)
(0.50,0.50,0.50)
F5
F51
F52
F53
F54
F51
F51
(0.50,0.50,0.50)
(0.63,0.68,0.72)
(0.55,0.65,0.75)
(0.56,0.75,0.91)
(0.50,0.50,0.50)
F52
(0.28,0.32,0.37)
(0.50,0.50,0.50)
(0.42,0.47,0.53)
(0.44,0.56,0.69)
(0.28,0.32,0.37)
F53
(0.25,0.35,0.45)
(0.47,0.53,0.58)
(0.50,0.50,0.50)
(0.52,0.59,0.66)
(0.25,0.35,0.45)
F54
(0.09,0.25,0.44)
(0.31,0.44,0.56)
(0.34,0.41,0.48)
(0.50,0.50,0.50)
(0.09,0.25,0.44)
F55
(0.00,0.21,0.44)
(0.22,0.39,0.56)
(0.25,0.36,0.48)
(0.41,0.45,0.50)
(0.00,0.21,0.44)
Table 8. Aggregation decision matrix of the risk dimensions from the second group
F1
F2
F3
F4
F5
F1
(0.50,0.50,0.50)
(0.47,0.53,0.60)
(0.53,0.66,0.78)
(0.44,0.64,0.84)
(0.48,0.74,1.00)
F2
(0.40,0.47,0.53)
(0.50,0.50,0.50)
(0.57,0.63,0.68)
(0.48,0.60,0.74)
(0.51,0.70,0.90)
F3
(0.22,0.34,0.47)
(0.32,0.38,0.43)
(0.50,0.50,0.50)
(0.41,0.48,0.56)
(0.44,0.58,0.72)
F4
(0.16,0.36,0.56)
(0.26,0.40,0.52)
(0.44,0.52,0.59)
(0.50,0.50,0.50)
(0.53,0.60,0.66)
F5
(0.00,0.26,0.52)
(0.10,0.30,0.49)
(0.28,0.42,0.56)
(0.34,0.40,0.47)
(0.50,0.50,0.50)
Table 9. Aggregation decision matrix of the risk factors from the second group
F1
F11
F12
F13
F14
F11
(0.50,0.50,0.50)
(0.58,0.67,0.76)
(0.33,0.53,0.73)
(0.36,0.68,1.00)
F12
(0.24,0.33,0.42)
(0.50,0.50,0.50)
(0.26,0.36,0.47)
(0.29,0.52,0.74)
F13
(0.27,0.47,0.67)
(0.53,0.64,0.74)
(0.50,0.50,0.50)
(0.53,0.65,0.77)
F14
(0.00,0.32,0.64)
(0.26,0.48,0.71)
(0.23,0.35,0.47)
(0.50,0.50,0.50)
F2
F21
F22
F23
F24
F21
(0.50,0.50,0.50)
(0.41,0.52,0.61)
(0.43,0.67,0.89)
(0.00,0.33,0.69)
F22
(0.39,0.48,0.59)
(0.50,0.50,0.50)
(0.52,0.65,0.78)
(0.09,0.31,0.57)
F23
(0.11,0.33,0.57)
(0.22,0.35,0.48)
(0.50,0.50,0.50)
(0.07,0.17,0.30)
F24
(0.31,0.67,1.00)
(0.43,0.69,0.91)
(0.70,0.83,0.93)
(0.50,0.50,0.50)
F3
F31
F32
F33
F34
F31
F31
(0.50,0.50,0.50)
(0.56,0.63,0.69)
(0.58,0.73,0.85)
(0.43,0.63,0.82)
(0.50,0.50,0.50)
F32
(0.31,0.37,0.44)
(0.50,0.50,0.50)
(0.52,0.60,0.65)
(0.37,0.50,0.63)
(0.31,0.37,0.44)
F33
(0.15,0.27,0.42)
(0.35,0.40,0.48)
(0.50,0.50,0.50)
(0.35,0.40,0.48)
(0.15,0.27,0.42)
F34
(0.18,0.37,0.57)
(0.37,0.50,0.63)
(0.52,0.60,0.65)
(0.50,0.50,0.50)
(0.18,0.37,0.57)
F35
(0.00,0.26,0.55)
(0.19,0.39,0.61)
(0.35,0.49,0.63)
(0.32,0.39,0.48)
(0.00,0.26,0.55)
F4
F41
F42
F43
F44
F41
(0.50,0.50,0.50)
(0.42,0.49,0.56)
(0.45,0.62,0.79)
(0.47,0.73,1.00)
F42
(0.44,0.51,0.58)
(0.50,0.50,0.50)
(0.53,0.63,0.73)
(0.55,0.74,0.94)
F43
(0.21,0.38,0.55)
(0.27,0.37,0.47)
(0.50,0.50,0.50)
(0.53,0.62,0.71)
F44
(0.00,0.27,0.53)
(0.06,0.26,0.45)
(0.29,0.38,0.47)
(0.50,0.50,0.50)
F5
F51
F52
F53
F54
F51
F51
(0.50,0.50,0.50)
(0.64,0.70,0.74)
(0.59,0.71,0.82)
(0.61,0.82,1.00)
(0.50,0.50,0.50)
F52
(0.26,0.30,0.36)
(0.50,0.50,0.50)
(0.45,0.51,0.57)
(0.47,0.61,0.76)
(0.26,0.30,0.36)
F53
(0.18,0.29,0.41)
(0.43,0.49,0.55)
(0.50,0.50,0.50)
(0.52,0.60,0.68)
(0.18,0.29,0.41)
F54
(0.00,0.18,0.39)
(0.24,0.39,0.53)
(0.32,0.40,0.48)
(0.50,0.50,0.50)
(0.00,0.18,0.39)
F55
(0.03,0.30,0.58)
(0.28,0.50,0.72)
(0.35,0.51,0.67)
(0.53,0.61,0.69)
(0.03,0.30,0.58)
Table 10. Impact degree and ranking of risk dimensions and factors for the two groups
Dimensions
/factors
Impact degree of
the first group
Ranking
Impact degree of
the second group
Ranking
F1
0.2481
1
0.2459
1
F11
0.3246
1
0.2968
1
F12
0.1829
4
0.2134
3
F13
0.2793
2
0.2784
2
F14
0.2133
3
0.2114
4
F2
0.2286
2
0.2306
2
F21
0.3301
1
0.2537
2
F22
0.2049
3
0.2432
3
F23
0.1737
4
0.1731
4
F24
0.2912
2
0.3300
1
F3
0.1749
4
0.1813
4
F31
0.1261
5
0.2559
1
F32
0.2266
2
0.2045
2
F33
0.1688
4
0.1684
4
F34
0.2520
1
0.2045
3
F35
0.2265
3
0.1667
5
F4
0.1813
3
0.1894
3
F41
0.2553
3
0.2930
1
F42
0.2777
1
0.2945
2
F43
0.2589
2
0.2325
3
F44
0.2082
4
0.1800
4
F5
0.1671
5
0.1528
5
F51
0.2673
1
0.2716
1
F52
0.1972
3
0.1934
3
F53
0.2074
2
0.1888
4
F54
0.1724
4
0.1500
5
F55
0.1557
5
0.1962
2
For experts in the first group, who are staff members of a technology enterprise, among the identified five dimensions, "Organization Function Risk" was found to be the most important risk dimension to influence software development performance and success. "Developing Technology Risk" and "Resources Integration Risk" are respectively the second and third highest impact dimensions affecting software development project performance. "Personnel System Risk" and "System Requirement Risk" are the last two dimensions in sequence in absolute and relative importance affecting performance and success of the development project.
For each dimension, among the first risk dimensions (F1), incapable organization management (F11) was considered the most impact risk factor, and it is also the most important factor for the global aspect. Among the second, fourth and fifth dimensions (i.e., F2, F4 and F5), immature technology development (F21), improper coordination with related divisions (F42) and continuing stream of requirements changes (F51) were all found to be the highest impact risk factors influencing the software development project success. Inavailability of key staff or project managers (F32) and lack of staff commitment, and low morale (F34), respectively are the most prominent risk factors which harm software development project success in each dimension.
The points of view in relation to the risk dimensions expressed by the experts of the second group, who are staff members of a software development company, are the same as the first group. For each dimension, the ranking results of the impact degree for risk factors are somewhat different from the first group. For example, among the first risk dimension (F1), incapable organization management (F11) is also considered as the most impacting risk factor in the first group, but it is the factor having the second greatest impact for global aspects, which is not the same as the first group. The most difference in ranking for factors of dimensions between these two groups is for the third risk dimension (F3). The second group considers reliance on a few key personnel (F31) as the most important factor in the "Personnel System Risk" aspect, but it has the least impact based on the evaluations of the first group.
5. Conclusions. Risk is an inherent component of software development projects. However, more preparation in advance will result in less loss in operation. Thus, in this study, five risk dimensions and an associated twenty-two risk factors were investigated. This can help the project managers with an overview of the global picture of risk. Then, we adopt the FLPR method which can simplify the AHP method and also avoid the necessity of checking the consistency in the decision-making process to assess project risk for software development. The questionnaire preparation and computation processes are easy to conduct regardless of which approach is used. The evaluation results of the experts in both groups implied that the impact degrees of the risk dimensions are the same whatever the rule on the software development project. Although there are some differences in the ranking of risk the factors for each risk dimension, the results should prove to be a valuable reference for software developers.
Based on the results of the models built to perform the risk management or plan response strategies for software development projects it can be seen that these models can benefit the stakeholders of software development projects and help them recognize what risk factors they face and to facilitate risk assessment and complete project risk management plans for the adequate allocation of resources.

Warning! This essay is not original. Get 100% unique essay within 45 seconds!

GET UNIQUE ESSAY

We can write your paper just for 11.99$

i want to copy...

This essay has been submitted by a student and contain not unique content

People also read