An entropy based approach for risk facto

International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number (2016) pp 2258-2262 © Research India Publications http://www.ripublication.com An Entropy Based Approach for Risk Factor Analysis in a Software Development Project Pradnya Purandare Assistant Professor & Research Scholar, Symbiosis Centre for Information Technology, Symbiosis International University, Pune, Maharashtra, India measure method of software project risk Although the relationship between software project performance and risks involved in it is iteratively examined, application of risk knowledge to mitigation is yet area to be explored since still remains some level of confusion among project and technical managers The conventional risk measurement method works with each risk factor’s loss as well as its probability of occurrence, which usually are assessed by experts subjectively Therefore, it indicates very high influence of artificial factors Also, it is difficult to realize the objective and effective measurement The literature studies indicate emphasis on risk factors, methods, processes, and uncertainties influencing project success [3-12] Especially the previous study with focus on Software Risk Management Principles, Practices and the Software project risks and their effect on outcomes, Identifying Software Project Risks with Delphi and other approaches surely puts more light on software project risk & their management [13-16] Though many methods, processes, techniques are researched and are being practiced by the industry but still there is a gap in software risks and project success This is also an indication of need of research of new techniques in the risk identification and assessment area In view of these reasons, this paper has proposed software development project’s risk measurement method with information entropy, and it significantly reduces the disadvantages of the subjective assessment in terms of occurrence probability and impact degree in previous studies In rapid digitization of the world, number of software development projects is increasing Also the customers are expecting virtues of development as minimum duration, minimum cost and high quality These virtues are exposed constantly to external & internal constraints of people and requirements changes during the development These all factors introduce risks and uncertainty to projects There are many techniques used to assess the risks But they introduce subjectivity in their assessment Hence there was a strong need of a technique which can be aptly used to assess the risks So the entropy concept can be suitably applied to the software project risks to assess them effectively The ever increasing demands of client to reduce cost, time and increase quality have put more pressure to have reduced risks and to have reduced uncertainties Hence we will check if Entropy can be used for risk assessment [17-21] The prime objective is to conduct risk assessment of software development projects The Software Project Risk management area has been studied & analyzed thoroughly by researchers & practitioners The primary work in this area has done by Barry Abstract Software development projects are mostly encountered by risks The risks emanates from different risk factors which are embedded in various activities of the project development There is no direct method of estimating the influence of each of the risk factors singularly or jointly for the actual Risk taking place Nevertheless the project management takes recourse to subjective judgment in assigning a percentage value of the influence of each of the risk factors in the software project since the success or failure or over run of the project is inseparably associated with risk The traditional approach for risk estimation follows the procedure of “guesstimate”-subjectively assigning probability of occurrence of risk caused by a certain risk factor No doubt this approach is subjective; however, these parameters can further be taken for viewing project risk from another perspective and possibly improvement in the metric of risk assessment The proposed method in this paper attempted to use the Shannon’s Entropy concept to measure the amount of information for estimating the software project risk from various risk factors Thus, each risk factor at a particular stage of development is identified and assigned a probability of causing a disruption This enables arriving in the entropy field for further analysis Keywords: Risk Factors in Software Projects, Shannon’s Entropy Introduction Software development projects are usually designed to progress in stages and each stage involves multiple activities These activities are prone to risks Several research papers on Risk Management in software projects have addressed the risk factors associated with the project [1] These factors if not identified properly become responsible for the success or failure of the project Various techniques of risk identification [2] and categorization [6-9] have been dealt with analyzing the remedial approach as well However, the very fact that risk by nature is uncertain leaves the software projects to some element of chance Software project risk management identifies, assesses controls and mitigates the risk factors which impact project success adversely The risk management can reduce risks Risk assessment & measuring alone can provide the data to help project decisions objectively Focus of early literature studies have been on software project risk assessment and control To date, yet there is lack of wide acceptance of particular 2258 International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number (2016) pp 2258-2262 © Research India Publications http://www.ripublication.com Step 2: Calculate Pij, decision matrix is standardized normalized Step 3: Calculate the Entropy Weight Ej Step 4: Table4 for FINAL SCORES Step 5: Decision on Risk Ranks Boehm and researchers in identifying critical risk factors which make impact on the project success or failures Then Risk management standards & methodologies adopted are PMI, Prince II, and ISO etc Lot of Risk management tools, software’s are used by industries And to the dismay in spite of all these efforts in the area of risk assessment & management, more than 35% projects are successful contributing to one of the top reasons being risk assessment & management Also the existing risk assessment techniques have their own subjectivity issues So we thought of introducing, studying & analyzing the entropy concept to assess risks and to check if it can be suitable and make risk assessment efficient [17-21, 23] Analysis We applied the above Entropy method to software project’s dataset It is consisting of risk factor values Above entire process of Entropy method was applied to these projects a w could arrive at risk ranks It yields us in understanding the uncertainty levels very clearly through these projects Software Project Risk Uncertainty degree based on Entropy Process [13-14, 22] Entropy Process To gather Original Data with Project Risk factor impacts To calculate Pij To calculate the Entropy Weight Ej To calculate attribute weightt wi To calculate the FINAL Risk SCORES The Risk Evaluation System of Software Project Risk The Establishment of Index System According to the risk factors that are existing in the software project, the paper analyses all links that affect certainty, safety of the project from internal and external project risk aspects [13-15, 22] To calculate the importance of the software project risk indicators in evaluation system decision-making data is obtained through research which constructs the decision matrix R={rij}mxn Where, m is the total number of projects considered and n is the total number of attributes rij is the risk factor at ith project and jth attribute  rij Step 1: Gather Original Data with Software Development Project Risk factors  Enter each project details as risk variable values per project  To take sum(Xij) of that each attribute across all project Step 2: Calculate Pij: decision matrix is standardized normalized  Each project’s each attribute’s value is calculated as Pij=Xij / Sum(xij) from previous table1  Mathematical Expression: Pij=Xij / sum(xij) rij=rij / sum (rij) (1) m  rij i 0 The normalized matrix is R=(rij) mxn Calculate the information entropy of index, the specific calculation formula is (1) Ei  1 m ln n rij ln rij Step 3: Calculate the Entropy Weight Ej  Each project’s each attribute’s value is calculated as Pij=P ij * Log(Pij) from previous table2’s corresponding values  Then sum (P ij * Log(Pij)) is calculated for each attribute variable value across projects  Then for each attribute variable, single Ej value is calculated as per below given mathematical expression  N is the total number of projects considered to calculate the entropy  Mathematical Expression Ej=-1 / log n * sum (P ij * Log(Pij)) wher i=1 to n (2) j 1 Where i=1, 2, …, m Calculate the weights of indicators, the specific formula is: wi   Ei n  (1  Ek ) (3) k 1 Where, E represents the entropy weight Weight vector of indicators is w=(w1,w2, wn) Here, weight can be defined as the importance of that risk indicator in entropy calculation According to the above steps, we can draw evaluation weights of software project risk indicators Step 4: attribute wt wi calc  Calculate 1-Ej from Ej values of Step3 Table  Calculate sum(1 – Ek) i.e K=1 to m from Ej values per attribute k=1 to m of Step3 Table  Mathematical Expression wi=(1-Ej) / sum(1-Ek) where k=1 to m Methodology: Step 1: Software Projects Risk factor wise original data is gathered Step 5: Table for FINAL ENTROPY, Risk SCORES 2259 International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number (2016) pp 2258-2262 © Research India Publications http://www.ripublication.com     the software projects We have converted the risk factor’s linguistic levels numerical values for further calculations [7] To calculate with zi(w)' sum, Sum(rij) is referred here from corresponding each value from the Table2 as Pij Wi value is available per attribute from the Step4 i.e From Table4 Each attributes sum is calculated, it is the Rank Score of Risk Entropy calculated finally Based on these Ranks scores decision of entropy levels and risk levels can be taken Mathematical Expression zi(w)=sum(rij) * wj where j=1 to m Table Attributes Here we have gathered the original project data with Risk factor values, which were converted from linguistic to numerical values All these risk factors are pre-defined by that cocomo-sdr dataset Risk factors for the parameters RELY, DATA CPLX, RUSE, DOCU, TIME, STOR, PVOL, ACAP, APEX, PCAP, PLEX, LTEX, PCON, TOOL, SITE, SCED, PREC, FLEX, RESL, TEAM are taken into consideration in the dataset The project ID shows the12 projects of the dataset for these risk factor parameters The tables show the above risk factor parameters as v1 to v22 [13-15, 22] In this step, each project’s details are mentioned as the risk variable values per project Then we have taken sum (Xij) of that each attribute across all projects Application Example of Entropy on Software Projects Risk Assessment The Establishment of Index System We have tried to experiment the above entropy method to calculate uncertainty degree of risks of software projects The dataset is of software development projects available in public domain The dataset consists of risk factor impact values of Table 1: Attributes of Software Projects Risk factor wise original data Project ID Project ID 10 11 12 sum(Xij) v1 v2 v3 v4 v5 v6 v7 v8 v9 v10 v11 v12 v13 v14 v15 v16 v17 1 0.95 0.91 1.29 1.05 0.85 0.81 0.76 0.91 0.91 0.81 0.9 1.09 1.1 0.87 0.95 0.91 1.29 1.17 1 0.76 0.85 0.91 0.81 0.9 1.22 1.1 1.28 0.87 0.95 0.91 1.29 1.17 1 0.88 0.85 0.84 0.81 0.9 1.22 1.1 1.14 0.87 0.95 0.91 1 1.15 0.85 0.88 0.88 0.85 0.91 1.29 0.93 0.92 1.34 1.15 1.23 1.11 1 0.85 0.88 1 0.9 0.93 0.92 0.9 1 1 0.87 0.71 0.76 0.91 0.91 0.9 1.17 0.8 0.92 0.9 0.87 0.95 0.91 1 0.87 0.71 0.76 0.85 0.84 0.81 1.17 0.93 v1 v2 v3 v4 v5 v6 v7 v8 v9 v10 v11 v12 v13 v14 v15 v16 v17 0.92 0.9 0.73 0.95 0.81 1.29 1.17 0.87 1.19 0.88 1 0.81 0.86 0.82 0.87 0.95 0.91 1 0.87 1.19 0.88 0.88 0.91 0.91 0.81 0.93 0.82 1.14 1 1.29 1.17 0.87 1.19 0.88 0.76 0.85 0.91 0.81 1 0.92 1 1 1 0.87 0.71 0.88 0.91 0.81 0.78 0.86 0.92 0.9 1.17 1.07 1.11 1 0.87 1.19 0.88 0.88 0.91 0.81 0.78 0.86 11.46 12.16 10.59 11.87 11.61 13.56 12.73 11.24 11.44 11.33 9.96 10.98 10.96 10.38 11.6 11.63 12 v18 v19 v20 v21 v22 6.2 3.04 5.65 2.19 7.8 3.04 5.65 7.8 3.04 5.65 2.19 7.8 2.03 2.83 2.19 4.68 3.72 3.04 5.65 2.19 4.68 4.05 0 3.12 0 3.29 3.12 v18 v19 v20 v21 v22 3.72 5.07 3.29 6.24 6.2 5.07 4.24 5.48 6.24 3.72 5.07 4.38 6.24 2.48 3.04 2.19 6.24 4.96 3.04 4.24 2.19 4.68 31 39.53 33.91 29.58 68.64 Table Attributes Here, we have calculated Pij to create a decision matrix which is standardized & normalized Each project’s, each attribute’s value is calculated as Pij=Xij / Sum(xij) from previous table1, as Pij=Xij / sum(xij), rij=rij / sum (rij) Table 2: Attributes calculate Pij Project ID 10 Project ID 11 12 sum(rij) v1 v2 0.087 0.082 0.095 0.0822 0.095 0.105 0.095 0.093 0.08 0.082 0.08 0.074 0.080 0.074 0.080 0.074 0.071 0.082 0.071 0.0937 v1 v2 0.08 0.082 0.08 0.074 1 v3 v4 v5 0.080 0.078 0.082 0.080 0.0783 0.082 0.080 0.078 0.082 0.080 0.078 0.126 0.096 0.105 0.094 0.084 0.086 0.082 0.08 0.078 0.068 0.08 0.069 0.082 0.08 0.078 0.094 0.084 0.086 v3 v4 v5 0.094 0.084 0.086 0.11 0.09 0.095 1 v6 v7 v8 v9 v10 v11 v12 v13 v14 v15 v16 v17 v18 v19 v20 v21 0.095 0.082 0.088 0.074 0.071 0.076 0.082 0.083 0.078 0.077 0.093 0.083 0.2 0.076 0.166 0.074 0.095 0.091 0.088 0.087 0.088 0.076 0.077 0.083 0.078 0.077 0.104 0.083 0.076 0.166 0.095 0.091 0.088 0.087 0.088 0.088 0.077 0.076 0.078 0.077 0.104 0.083 0.076 0.166 0.0740 0.073 0.078 0.102 0.074 0.077 0.088 0.077 0.083 0.124 0.086 0.079 0.083 0.051 0.083 0.074 0.081 0.078 0.088 0.074 0.088 0.088 0.091 0.091 0.086 0.086 0.079 0.083 0.12 0.076 0.166 0.074 0.073 0.078 0.077 0.062 0.088 0.076 0.082 0.083 0.086 0.10 0.068 0.083 0.102 0 0.073 0.078 0.077 0.062 0.088 0.076 0.077 0.076 0.078 0.10 0.079 0.083 0 0.111 0.095 0.091 0.077 0.104 0.088 0.088 0.091 0.091 0.078 0.086 0.073 0.083 0.12 0.128 0.111 0.073 0.078 0.077 0.104 0.077 0.088 0.082 0.083 0.078 0.086 0.079 0.083 0.2 0.128 0.125 0.185 0.095 0.091 0.077 0.104 0.077 0.076 0.077 0.083 0.078 0.086 0.085 0.083 0.12 0.128 0.148 v6 v7 v8 v9 v10 v11 v12 v13 v14 v15 v16 v17 v18 v19 v20 v21 0.073 0.078 0.077 0.062 0.088 0.088 0.091 0.083 0.078 0.067 0.073 0.083 0.08 0.076 0.074 0.073 0.078 0.077 0.104 0.077 0.088 0.091 0.083 0.078 0.067 0.073 0.083 0.16 0.076 0.125 0.074 1 1 1 1 1 1 1 1 2260 v22 0.113 0.113 0.113 0.068 0.068 0.045 0.045 0.09 0.090 0.090 v22 0.09 0.068 International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number (2016) pp 2258-2262 © Research India Publications http://www.ripublication.com Table Attributes Here, we have calculated the Entropy Weight Ej Each project’s each attribute’s value is calculated as Pij=P ij * Log(Pij) from previous table2’s corresponding values Then sum (P ij * Log(Pij)) is calculated for each attribute variable value across projects Then for each attribute variable, single Ej value is calculated as per below given mathematical expression N is the total number of projects considered to calculate the entropy with Ej Table 3: Attributes for Entropy Weight Ej Project ID v1 v2 v3 v4 v5 v6 v7 v8 v9 v10 v11 v12 v13 v14 v15 v16 v17 v18 v19 v20 v21 v22 1 0.95 0.91 1.29 1.05 0.85 0.81 0.76 0.91 0.91 0.81 0.9 1.09 6.2 3.04 5.65 2.19 7.8 1.1 0.87 0.95 0.91 1.29 1.17 1 0.76 0.85 0.91 0.81 0.9 1.22 3.04 5.65 7.8 1.1 1.28 0.87 0.95 0.91 1.29 1.17 1 0.88 0.85 0.84 0.81 0.9 1.22 3.04 5.65 2.19 7.8 1.1 1.14 0.87 0.95 0.91 1 1.15 0.85 0.88 0.88 0.85 0.91 1.29 0.93 2.03 2.83 2.19 4.68 0.92 1.34 1.15 1.23 1.11 1 0.85 0.88 1 0.9 0.93 3.72 3.04 5.65 2.19 4.68 0.92 0.9 1 1 0.87 0.71 0.76 0.91 0.91 0.9 1.17 0.8 4.05 0 3.12 0.92 0.9 0.87 0.95 0.91 1 0.87 0.71 0.76 0.85 0.84 0.81 1.17 0.93 0 3.29 3.12 0.92 0.9 0.73 0.95 0.81 1.29 1.17 0.87 1.19 0.88 1 0.81 0.86 3.72 5.07 3.29 6.24 0.82 0.87 0.95 0.91 1 0.87 1.19 0.88 0.88 0.91 0.91 0.81 0.93 6.2 5.07 4.24 5.48 6.24 10 0.82 1.14 1 1.29 1.17 0.87 1.19 0.88 0.76 0.85 0.91 0.81 1 3.72 5.07 4.38 6.24 11 0.92 1 1 1 0.87 0.71 0.88 0.91 0.81 0.78 0.86 2.48 3.04 2.19 6.24 12 0.92 0.9 1.17 1.07 1.11 1 0.87 1.19 0.88 0.88 0.91 0.81 0.78 0.86 4.96 3.04 4.24 2.19 4.68 sum(Xij) 11.46 12.16 10.59 11.87 11.61 13.56 12.73 11.24 11.44 11.33 9.96 10.98 10.96 10.38 11.6 11.63 12 31 39.53 33.91 29.58 68.64 Table Attributes Here, we have calculated the attribute wt wi by from table3 values of Ej & Ek To calculate wi=(1-Ej) / sum(1-Ek) where k=1 to m Table shows wi calculation for example a few risk parameter’s wi calculation based on CPLX, DOCU, STOR, ACAP, PCAP,LTEX, TOOL, SCED For remaining all risk parameters same method is used to calculate wi Table 4: Attribute weight wi Hence, wi= 1-Ek sum(1-Ek) 0.002820076 CPLX DOCU STOR ACAP PCAP LTEX TOOL SCED 0.00350 0.05751 0.00102 0.00337 0.004263 0.00153 0.00244 0.01154 Table Attributes Here, we have to calculate FINAL ENTROPY and Risk SCORES with zi(w)' sum Each attributes sum is calculated, it is the Rank Score of Risk Entropy calculated finally Based on these Ranks scores decision of entropy levels and risk levels can be taken as zi(w)=sum(rij) * wj where j=1 to m Decision Making: More the Score indicates more Entropy value means less of risk, less score means more risk Table 5: Attributes for FINAL Risk SCORES Project ID v1 v2 v3 v4 v5 v6 v7 v8 v9 v10 v11 v12 v13 v14 v15 v16 v17 v18 v19 v20 v21 v22 0.00024 0.00028 8.22584 0.00026 0.00040 0.00012 0.00021 0.00085 0.0001 0.00011 0.00011 6.10392 0.00046 0.00033 0.00048 0.06669 0.00550 0.05372 0.01037 0.00268 0.00027 0.00028 0.00472 8.22584 0.00026 0.00040 0.00014 0.00021 0.001 0.00013 0.00011 0.00011 6.10392 0.00046 0.00033 0.00054 0 0.0055 0.05372 0.00268 0.00027 0.00036 0.00472 8.22584 0.00026 0.0004 0.00014 0.00021 0.001 0.00013 0.00012 0.00011 5.63439 0.00046 0.00033 0.00054 0 0.0055 0.05372 0.01037 0.00268 0.00027 0.00032 0.00472 8.22584 0.00026 0.00031 0.00012 0.00025 0.00085 0.00011 0.00012 0.00011 6.10392 0.00074 0.00037 0.00041 0 0.00367 0.02691 0.01037 0.0016 0.00022 0.00028 0.00727 9.9576 0.00035 0.00034 0.00012 0.00021 0.00085 0.00013 0.00012 0.00012 6.70761 0.00051 0.00037 0.00041 0.04001 0.0055 0.05372 0.01037 0.0016 0.00022 0.00025 0.00543 8.65878 0.00029 0.00031 0.00012 0.00018 0.00071 0.00013 0.00011 0.00011 6.10392 0.00051 0.00044 0.00035 0 0.00733 0 0.00107 0.00022 0.00025 0.00472 8.22584 0.00026 0.00031 0.00012 0.00018 0.00071 0.00013 0.00011 0.00011 5.63439 0.00046 0.00044 0.00041 0 0 0.01558 0.00107 0.00022 0.00025 0.00396 8.22584 0.00023 0.00040 0.00014 0.00018 0.0012 0.00013 0.00012 0.00012 6.70761 0.00046 0.00037 0.00038 0.04001 0.00918 0.01558 0.00214 0.00020 0.00028 0.00472 8.22584 0.00026 0.00031 0.00012 0.00018 0.00120 0.00011 0.00012 0.00011 6.10392 0.00046 0.00037 0.00041 0.06669 0.00918 0.04031 0.02595 0.00214 10 0.0002 0.00032 0.00543 8.65878 0.00029 0.0004 0.00014 0.00018 0.0012 0.00011 0.00011 0.00011 6.10392 0.00046 0.00037 0.00044 0.04001 0.00918 0.02074 0.00214 Project ID v1 v2 v3 v4 v5 v6 v7 v8 v9 v10 v11 v12 v13 v14 v15 v16 v17 v18 v19 v20 v21 v22 11 0.00022 0.00028 0.00543 8.65878 0.00029 0.00031 0.00012 0.00018 0.00071 0.00013 0.00012 0.00012 6.10392 0.00046 0.00029 0.0003849 0.02667 0.00550 0.01037 0.00214 12 0.00022 0.00025 0.00635 9.26489 0.00032 0.00031 0.00012 0.00018 0.0012 0.00011 0.00012 0.00012 6.10392 0.00046 0.00029 0.00038 0.05335 0.0055 0.04031 0.01037 0.00160 FINAL 0.00282 0.0035 0.05751 0.00102 0.00337 0.00426 0.00153 0.00244 0.01154 0.00152 0.00146 0.00141 0.00073 0.00595 0.00437 0.0052 0.33345 0.07164 0.32246 0.14012 0.02359 SCORE : SUM RANK Least RISK Risk Rank 2261 International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number (2016) pp 2258-2262 © Research India Publications http://www.ripublication.com [10] Y.H.Kwak, J Stoddard, 2004, “Project Risk Management: Lessons Learned from Software Development Environment,” Elsevier ScienceDirect Technovation, Vol 24, pp 915-920 [11] H Steyn, 2002, “Project Management applications of the theory of constraints beyond critical chain scheduling,” Elsevier ScienceDirect International Journal of Project Management, Vol 20, pp 75-80 [12] T Raz, 2001,”Use and benefits of tools for project risk management,” Elsevier ScienceDirect International Journal of Project Management, Vol.19:9-17 [13] J.X ZHAO, M LIU, L LI, 2015, “Comprehensive Evaluation of Metro Project Bidding Risk Based on Entropy Value Method and Fuzzy,” Proc International Conference on Industrial Technology and Management Science, Qingdao, China, pp 1170-1172 [14] Xiaohua Zou, 2014, “Research on Comprehensive Evaluation of CCS Project Based on Integrated Cloud Model and Entropy Weight,” International Journal of Nonlinear Science, Vol 18, pp 53-59 [15] Zhu Weidong, Liu Jingyu, 2014,”The Application of Information Entropy Theory in Project Evaluation Based on Multiple Attribute Decision Making Context,” Sensors & Transducers, Vol 172, pp 301-307 [16] Roy Schmidt, Kalle Lyytinen, Mark Keil, Paul Cule, 2001, “Identifying Software Project Risks: An International Delphi Study,” Journal of Management Information Systems, Vol.17, pp 5-36 [17] http://www.brighthub.com/office/projectmanagement/articles/48245.aspx [18] Barry Bohm, 1991, “Software Risk Management: Principles and Practices,” IEEE Software, Vol 8, pp.3241 [19] Linda Wallace, Mark Keil, 2004, “Software project risks and their effect on outcomes,” Communications of the ACM-Human-computer etiquette, Vol 47, pp 68-73 [20] Tom Addison, etal, 2002,”An empirical study of methods used by experienced project managers,” Proc Annual research conference of the South African institute of computer scientists and information technologists on Enablement through technology South African Institute for Computer Scientists and Information Technologists, Republic of South Africa, pp 128 – 140 [21] Ammar Ahmed, Berman Kayis, Sataporn Amornsawadwatana, 2007, “A review of techniques for risk management in projects Benchmarking,” An International Journal,Vol 14, pp 22-36 [22] https://terapromise.csc ncsu.edu: 8443/svn/repo/effort/ cocomo/cocomo2/cocomo-sdr/cocomo-sdr.arff [23] Pradnya Purandare, 2012 “Enhanced IT project risk management process framework,” Journal of Computer Science and Engineering, Vol.13, pp 21-28 Decision Making: More the Score indicates ore Entropy value means less of risk, less score means more risk Conclusion The application of Entropy to Software Project risks definitely helps in understanding the uncertainty level & degree of risks in those software projects Hence, entropy techniques shows promising way to assess the risk uncertainty degree ahead, and we will take further research work in the area of software project risks based on information entropy technique Acknowledgements My sincere thanks to Phd Guide Dr Prasenjit Sen, Professor Symbiosis International University, Pune, India for the invaluable inputs and guidance on introduction of Shannon’s Entropy concept to software project risk management research References [1] [2] [3] [4] [5] [6] [7] [8] [9] Roy Schmidt, etal, 2001, “Identifying Software Project Risks: An International Delphi Study,” Sensors & Transducers, Vol 17, pp 5-36 Gary Stoneburner, etal, 2002, “Risk Management Guide for Information Technology Systems,” Proc ACM Technical Report SP 800-30 National Institute of Standards & Technology, Gaithersburg, MD, United States, pp i-F-1 James J Jiang, Gary Klein, T Selwyn Ellis2012, “Measure of Software Development Risk,” Project Management Journal, The Journal of PMI, Vol 33-3, pp 30-41 “Project Risk Management Process Framework,” PMI PMBOK® David Hillson, 2002, ”Extending the risk project to manage opportunities,” Elsevier ScienceDirect International Journal of Project Management, Vol 20, pp 235-240 K.A Artto, 1997, “Putting Project Risk Management Into Perspective Fifteen years of project risk management applications-where are we going?,” Proc: Managing Risks in Projects Helsinki, Finland: Proc IPMA Symp, Project Management, Vol 2002, “Risk Management Guide for Information Technology Systems,” Proc: ACM Technical Report SP 800-30 National Institute of Standards & Technology, Gaithersburg, MD, United States, pp i-F-1 Roger Atkinson, Lynn Crawford, Stephen Ward, 2006, “Fundamental uncertainties in projects and the scope of project management,” ELSVIER, SCIENCEDIRECT, Inter-national Journal of Project Management, Vol 24, pp 687-698 Mark Keil, Amrit Tiwana, Ashley A Bush, 2002, “Reconciling user and project manager perceptions of IT project risk: A Delphi Study,” Info Systems J, Vol 12, pp 103-119 2262