Resource Decisions in Software Development Using Risk Assessment Model

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1 Proceedings of the 39th Hawaii International Conference on System Sciences - 6 Resource Decisions in Software Development Using Risk Assessment Model Wiboon Jiamthubthugsin Department of Computer Engineering Faculty of Engineering Chulalongkorn University wiboon@gmail.com Daricha Sutivong Department of Computer Engineering Faculty of Engineering Chulalongkorn University Daricha.S@Chula.ac.th Abstract The resource decisions in software project using cost models do not satisfy managerial decision, as it does not support trade-off analysis among resources. A Bayesian net approach enables this analysis; however, it requires discretization of parameter values and thus sacrifices accuracy. Although narrow intervals can alleviate this problem, the number of states grows quickly with the demanded accuracy. The Bayesian approach also requires pre-setting of the measurement scale, which may not be applicable to all users. In this paper, we propose using a risk assessment model to aid software resource decisions. The methodology employs a continuous function that captures key parameters of software development, such as development time, staff productivity, requirement volatility and software complexity. Using the model, users can perform trade-off analysis among various resource allocations and outcomes. We also propose integrating this model with optimization to solve complicated problems, which can be accomplished straightforwardly with the proposed methodology.. Introduction Software project management begins with project planning. Before the project begins, the project manager and the software team must estimate the resources that will be required and the time that it will take from start to finish [4], that is how much effort is needed to complete the project. The approaches for cost and effort estimation can be categorized into three types: expert judgment, algorithmic models and machine learning [, 6]. Expert judgment is a technique that produces estimates based on an expert s previous experience on similar projects []. A disadvantage of expert judgment is that it is unrepeatable, and an expert judgment estimation relies on quality of experts [6]. When a problem is complicated, algorithmic models using traditional metric approaches, such as regression-based models, are widely used in cost estimation. They solve complicated problems but still do not support managerial decision making in software projects. Specifically, they do not allow a project manager to trade off among resources. Fenton, et al. [5] use a Bayesian network (a type of machine learning) which solves this problem and allows for trade-off analysis. Nonetheless, there are some limitations in the Bayesian method proposed by Fenton, et al. Since the input values are represented in intervals, the methodology sacrifices accuracy and user s flexibility in specifying inputs in order to keep the number of states manageable. To address these challenges, we propose employing a risk assessment model in order to evaluate software resource decision. This model also accommodates integration of optimization and an introduction of constraints into the resource decision problem. A risk assessment model specifies the relationship among parameters using probability distribution. The model offers accuracy due to continuous values of inputs and outputs. Users also have flexibility to change scales of measurement to those that fit their organizations. This approach enables a manager to assess the effect of various adjustments in terms of probability, such as probability of project completion. For example, increasing development time from months to months may increase the probability of project completion by 5%. We also study relationships among various resources to understand trade-off between them. For example, to reduce the project time, should we increase staff productivity or decrease requirement volatility? Section briefly summarizes background knowledge relevant to this study. Section 3 describes our proposed approach and analysis steps to resource /6/$. (C) 6 IEEE

2 Proceedings of the 39th Hawaii International Conference on System Sciences - 6 decision problem. The model is validated using COCOMO II in section 4, and section 5 concludes the study.. Background This section describes knowledge and principles relevant to the proposed approach. Section. summarizes evolutionary process models of software development that the risk assessment model is designed for. Software risk assessment process is described in section.. Section.3 discusses the Bayesian net approach to resource decision and their limitations as a motivation behind this study... Evolutionary process models Software requirements often change during development leading to the need for a process model that is designed for products that evolve over time [4]. The evolutionary method is based on the following principle: deliver something to a real enduser, measure the added-value to the user in all critical dimensions and adjust both design and objectives based on observed realities [8]. eliminate risks and build an early partial version of software on which a customer can give feedbacks. The spiral model of the software process is shown in figure. The spiral model is suitable for development of large-scale software because software evolves as the process progresses. This paper explores a resource decision problem for a software development project using evolutionary process model... Software risk assessment Risk assessment is a part of risk management. The steps in risk assessment are risk identification, risk analysis and risk prioritization []. Risk identification aims to generate the risk items in the software project. Examples of risk items are developing the wrong user interface, shortfalls in externally performed tasks, etc. Techniques for risk identification are checklists, examination of decision drivers, comparison with experience and decomposition. Risk analysis evaluates the probability that a loss occurs and the loss size. Techniques of risk analysis are performance models, cost models, network analysis, statistical decision analysis and quality-factor analysis. Risk prioritization ranks risk items according to their importance. Techniques of risk prioritization include risk-exposure analysis, risk reduction leverage analysis and Delphi techniques. As an example, assume that a risk item in the project is a requirement misunderstanding with of a probability of occurrence of.4 and the loss of $5 if this event occurs. Using a risk-exposure technique, the risk exposure of an item is $ (risk exposure is the probability multiplied by its loss). If $ is the highest risk exposure, this risk item will be ranked at the top..3. Resource decision with Bayesian nets Figure. Spiral model of the software process [3] An example of evolutionary model is a spiral process model. The process recognizes the need to visit the sequence of requirement analysis, design, implementation and test more than once each iteration is called a cycle [4]. This process helps Software metrics should support managerial decision making in software projects and allow a project manager to trade off the resources used against the outputs in a software project [5]. For example, a software project manager may choose to increase quality despite the cost, or s/he may choose to reduce quality of requirement from perfect to average in order to accommodate lower quality of process and people. Fenton proposed the Bayesian net to handle these analyses. The nodes in the Bayesian net represent resources in a software project, such as the number of people and project duration. Software resources can be estimated by entering requirements for resources such as the number of function points and then observe the values of the nodes we want to predict or perform trade-off analysis.

3 Proceedings of the 39th Hawaii International Conference on System Sciences - 6 Although this method supports decision-making, the values of all nodes are specified in terms of intervals, so accuracy may be lost. For instance, intervals of project duration may be. 3., , and so forth. If an answer is 3. 6., we only know that the project duration is between 3. and 6., but what is the exact project duration? The breakpoints of intervals are also difficult to determine appropriately. Although this problem can be solved using narrow intervals, the number of states increases rapidly along with the number of conditional probabilities of the Bayesian net that must be determined. Moreover, different users may want to specify different scales to measure variables; for example, one user may want to measure quality of process and people as low, average and high, while another may want the scales to be poor, very low, low, average, high, very high and perfect. 3. Approach and analysis This section describes the proposed approach and analysis steps to software resource decisions. The risk assessment model underlying our study is described in section 3.. Section 3. explores the relationship among key factors implied by the model. Section 3.3 illustrates how the model is used for resource allocation. Finally, an integration of optimization to the model is presented in section Risk assessment model There are many risk assessment models. For examples, heuristic risk assessment [9], software risk assessment by Foo [7] and risk management framework by Roy [5]. However, these models use subjective values which cannot be analyzed automatically from observed parameters in the environment. Nogueira et al. [-3] construct a formal risk assessment model for software evolutionary process. The factors used in the model are requirement volatility, staff productivity, software complexity and development time. The model is based on an assumption that evolutionary cycles can be modeled by Weibull s family distribution. Specifically, in the risk assessment model, the development time is characterized by the probability density function (pdf) in () and the cumulative distribution function (cdf) in (). x is a random variable representing a development time; is a shape parameter of the distribution describing staff productivity; β is a scale parameter of the distribution accounting for requirement volatility; γ is a shift of the curve to the right representing the already discovered software complexity. f ( x; γ,, β) = F ( x; γ,, β ) =, x < γ β ( x γ ) x γ β () exp, x γ, x < γ x γ β exp, x γ Parameters are calibrated according to the study by Nogueire [3]. Staff productivity () is defined as a productivity ratio of the percentage of direct work time over the percentage of idle time. For high productivity, is between. and 6.; for low productivity, is between.8 and.. Requirement volatility describes the total rate at which new requirements emerge and current requirements are discarded. Specifically, requirement volatility (β) is defined as INT((BR + DR)/) where BR (birth rate) is a percentage of new requirements incorporated in each cycle of evolutionary process and DR (death rate) is a percentage of requirements dropped by the customer in each cycle of evolutionary process. Complexity is a development time that is measured from Large Granularity Complexity (LGC), which is a metric expressed as the number of operators (O), data streams (D), and types (T) found in formal specification written in Prototyping Specification Design Language (PSDL). Software complexity (γ) in months is defined as 3 * Ln(LGC) - 8 which can be converted to KLOC following the relationship: KLOC = (3 * LGC + 5) / (for Ada code). Development time of a project is described by the probability of the project completion within or equals to x months, using the cdf in (). The risk assessment model proposed by Nogueira has a practical advantage, as the model parameter values (, β, γ and x) are standardized and can be observed in a real software development environment. Moreover, the model yields the probability of finishing the project by a given time (x), which is useful in assessing the risk of an overall project. 3.. Relationship among key factors () The key factors of the risk assessment model are requirement volatility, staff productivity, software 3

4 Proceedings of the 39th Hawaii International Conference on System Sciences - 6 complexity (months) and development time. Because software size (KLOC) can be derived from complexity, and software size is an important factor in software project management, we also include software size in the variable set under investigation. In order to understand trade-off between key factors, relationships among parameters that drive the risk assessment model are analyzed. An equation is formulated with each driving factor as a function of the remaining parameters. We also pick a pair of variables under study and plot graph to display the relationship between them while keeping the other variables constant. Sensitivity analysis is performed between high and low values given in table, while variables not under investigation assume medium values. Table. Parameter values used for analysis Variables Low Medium High 4 β γ (months) KLOC x (months) values are determined from the high and low productivity scenarios proposed by Nogueira. If is, the amount of direct work time is two times the amount of idle time. β is determined from a birth rate and a death rate of requirement during each cycle in evolutionary process. For example, when β is 5., on average in each cycle of evolutionary process, the sum of the percentage of new requirements incorporated and the percentage of requirements dropped by the customer is 5%. γ is determined from LGC; γ equals 43 months (or LGC = 5,5) implies that software complexity causes 43 months in development time. KLOC follows directly from γ value according to the previously mentioned relationship. A development time (x) is calculated from medium values of, β and γ following the distribution in () at the project completion probability of 99%. In other words, when x is 53, the project will be completed within 53 months with the probability of 99%. Possible pairs for relationship study are (,β), (,γ), (,KLOC), (,x), (β,γ), (β,kloc), (β,x), (γ,x) and (KLOC,x). Many of the relationships are similar in nature. We illustrate the followings as examples of analysis which could be repeated for any pair. To investigate, the cdf of the risk assessment model is transformed to the following: (productivity) = 9% 8% 7% ln ln x ln γ β P β (requirement volatility) Figure. Relationship between β and (γ=43, x=53) Figure displays the relationship between staff productivity (), and requirement volatility (β) for various probabilities of project completion (9%, 8% and 7%). As we increase requirement volatility, we have to increase staff productivity in order to maintain the same probability of success. The graph grows almost similar to an exponential function, which implies that in a more volatile environment (in requirement change), a larger productivity increase is needed to maintain the same probability of project completion. Also, in a more volatile environment, increasing the probability of project completion is substantially more difficult. To study β, the cdf of the risk assessment model is transformed to the following: β = x γ ln P From figure 3, a higher complexity leads to a decrease in requirement volatility, as a project manager has to trade off one factor for the other in order to maintain the same probability of project (3) (4) 4

5 Proceedings of the 39th Hawaii International Conference on System Sciences - 6 completion. Linear relationship between the two parameters shows that this trade-off rate is independent of the complexity or the requirement volatility levels. β (requirement volatility) γ (complexity) 9% 7% 5% 3% % Figure 3. Relationship between γ and β (x=53, =) To study x, the cdf of risk assessment model is transformed to the following: x β ln + γ P = (5) Figure 4 shows that when software size increases, we have to extend development time to maintain the same probability of project completion. The graph is a logarithmic function which indicates that as software size gets larger, an incremental increase in development time gets smaller Resource decisions The risk assessment model allows a project manager to make tradeoff decisions among resources and outcomes. We show analysis steps in the following examples: The first example is presented in figure 5. Suppose we have a project that must be completed in 53 months. We measure software complexity from a formal specification and convert it to γ of 43 months. We would like the probability of project completion to be 99%. Figure 5 shows that if the requirement volatility is 6, we must maintain staff productivity at 3, i.e. direct work time has to be 3 times the idle time. Suppose that due to operational constraints, the productivity is limited to.5, we may choose to limit requirement volatility to 5. If requirement volatility cannot be reduced, we will have to accept a lower probability of project completion at 96%. The probability of project completion also helps us realize the effect of resource adjustment to an overall project. Probability of project completion (%) Requirement volatility = 5 Requirement volatility = productivity Figure 5. Productivity needed for various requirement volatilities x (months) % 7% 5 5 KLOC Probability of project completion productivity = productivity = development time (months) Figure 4. Relationship between KLOC and x (=, β=5) Figure 6. Development time needed for various productivity levels 5

6 Proceedings of the 39th Hawaii International Conference on System Sciences - 6 In the second example, shown in figure 6, we consider a project with requirement volatility of 5 and software complexity of 43. If staff productivity is, the project will take approximately 66 months at the completion probability of 99%. Assume that we need to finish the project in 57 months, we may choose to increase staff productivity to.5. If the productivity can not be increased, we will have to accept a lower probability of project completion at 94%. Rate of change in project completion probability productivity = productivity = development time (months) Figure 7. Rate of change in project completion probability vs. development time The rates of change in project completion probability also guide the decision process, as shown in figure 7, which uses the same parameter values as in the second example. At 6 months, extending the project by one additional month rarely increases the probability of success (less than.). In contrast, if the project is previously constrained at 45 months, extending the deadline by one month can increase the probability of project completion by 3% when productivity is and by 5% when productivity is.5. Note that higher productivity leads to higher incremental gain in probability of success when the development time is tightly constrained to be too low. As the development time gets larger, the incremental gain in probability of project completion becomes minimal because the project is already likely to be completed Integrating the model with optimization One advantage of the proposed method is the ability to integrate optimization to the model in order to solve more complicated problems. The approach entails converting resource decision problems to nonlinear programming and solving it to find an optimal resource allocation strategy that achieves our desired objective under constraints. Observe that without resource and economic constraints, the problem becomes trivial. For example, suppose our objective is to maximize the project completion probability, a straightforward solution is to use maximum productivity, limit requirement volatility to minimal, and allow for maximum development time. To make the problem realistic, besides resource constraints, cost of resources is introduced to the problem. The objective then becomes maximizing the return of the software development project by allocating the resources optimally under constraints. For example, suppose we are constrained by requirement volatility and software complexity, what are the development time and staff productivity that we should choose to optimize the return? The problem formulation and assumptions are described below. Note that one may explore other variations or relax certain assumptions, while the same solution steps still apply. Assume that a salary cost for all employees at productivity of is $s/month. Assume also that an increase in productivity leads to a linear increase in cost, i.e. no overtime (higher) rate. Therefore, a cost function is as follows: cost = s x (6) Assume that once the project is completed, we receive income i. The probability of project completion is p. Thus, expected income of the project (expin) is shown in (7). The expected return of the project is (8). Observe that the trade-off between cost and probability of project completion is automatically encoded in cost and p, which are driven by the same underlying parameters. expin = p i (7) E(return) = expin cost (8) The problem is converted into nonlinear programming with necessary constraints in order to solve for an optimal solution. As an example, suppose a project has five staff members, each with a salary of $6,/month leading to the total salary of $3,/month. The project income, if completed, is $5,. The project development time cannot exceed months. Requirement volatility is 5, and complexity is month. Thus, the expected return of this project is shown below: x 5 ( ) = 5, exp E return 3,x, x (9) 6

7 Proceedings of the 39th Hawaii International Conference on System Sciences - 6 The expected return is shown in figure 8, 9 and. The nonlinear optimization is: Maximize return Subject to. 6 x Using software tool for nonlinear optimization, an optimal solution is =. and x = 3.5 months, and the project expected return is $3,653. Note that the optimal productivity ratio is., which is at the boundary of the constraint and is less than the average-case productivity (.). This result may arise from the fact that x leads to an increase in p faster than does, while both have the same impact on cost. Therefore, it is optimal to increase development time (x) while keeping the productivity () to minimal in this particular case. Expected Return (Dollar) x 6 Time (months) Productivity Ratio Figure 8. Expected return as a function of productivity and development time Expected Return (Dollar) 4 x x Time (months) Figure 9. Expected return vs. time (=.) Expected Return (Dollar) x Productivity Ratio Figure. Expected return vs. productivity (x=3) 4. Validation Nogueira validated the risk assessment model under study with COCOMO 8, which is an older version of COCOMO. To ensure its validity with the current environment, we revalidate it with COCOMO II [] the current standard cost model. Parameter values for a normal scenario from table are used: productivity =, requirement volatility = 5 and KLOC = 496 (LGC = 5,5 or software complexity = 43). Firstly, the development time is calculated using COCOMO II, and the parameters are mapped from Nogueira model to COCOMO II. In COCOMO II, requirement evolution and volatility are represented as a factor called REVL. ACAP and PCAP are factors related to analyst capability and programmer capability. Thus, requirement volatility in the risk assessment model is mapped to REVL in COCOMO II, and productivity ratio is mapped to ACAP and PCAP. Since the values are for normal case, ACAP and PCAP are rated to nominal, while REVL is defined as 5. COCOMO II yields a development time of about 5 months. Secondly, the development time from COCOMO II is used to obtain a project completion probability in the risk assessment model. Since the development time obtained from COCOMO II indicates the time that the project should be completed, the calculated project completion probability should be high. We obtain the project completion probability of 96%, which is reasonably high. We also validate the development time calculated by the risk assessment model at 99% completion probability and get 53 months, which is close to 5 months of COCOMO II. Thus, the risk assessment model yields similar results to COCOMO II, at least in the normal case. 7

8 Proceedings of the 39th Hawaii International Conference on System Sciences Conclusion This paper proposes using a risk assessment model to aid resource decisions in software projects. Because the model supports continuous values, users achieve accuracy and have flexibility in specifying their inputs. Parameters of the model can also be objectively measured in a real development environment. The proposed methodology allows for systematic trade-off analysis among various resources and outcomes, which is essential in project management. We also illustrate how to integrate nonlinear optimization to the proposed model to solve more complicated problems. The proposed analysis steps can be adapted to incorporate other risk assessment models that users may consider more appropriate for the software development process in their companies. 6. References [] B. W. Boehm, Software cost estimation with Cocomo II. Prentice Hall,. [] B. W. Boehm, "Software risk management: principles and practices", Software, IEEE, vol. 8, pp. 3-4, 99. [3] B. W. Boehm, "A spiral model of software development and enhancement", Computer, vol., pp. 6-7, 988. [4] E. J. Braude, Software engineering : an object-oriented perspective. Wiley,. [5] N. Fenton, W. Marsh, M. Neil, P. Cates, S. Forey, and M. Tailor, "Making resource decisions for software projects", Proceedings of the 6th International Conference on Software Engineering, 4. [6] N. E. Fenton and S. L. Pfleeger, Software metrics : a rigorous and practical approach, nd ed. International Thomson Computer Press ; PWS Publishing Co., 997. [7] S.-W. Foo and A. Muruganantham, "Software risk assessment model", Proceedings of the IEEE International Conference on Management of Innovation and Technology,. [8] T. Gilb and S. Finzi, Principles of software engineering management. Addison-Wesley Pub. Co., 988. [9] R. J. Madachy, "Heuristic risk assessment using cost factors", Software, IEEE, vol. 4, pp. 5-59, 997. [] E. Mendes, Mosley, N., and Watson, I., "A Comparison of Case-based Reasoning Approaches to Web Hypermedia Project Cost Estimation", Proceedings of the th International World-Wide Web Conference, Hawaii,. [] J. Nogueira and Luqi, "A Risk Assessment Model for Evolutionary Software Projects", Monterey Workshop on Modelling Software System Structures in a fastly moving scenario, Santa Margherita Ligure, Italy,. [] J. C. Nogueira, Luqi, and S. Bhattacharya, "A risk assessment model for software prototyping projects", Proceedings of the th International Workshop on Rapid System Prototyping,. [3] L. Nogueira Juan C., Berzins Valdis, Nada Nader, "A Formal Risk Assessment Model for Software Evolution", Second International Workshop on Economics-Driven Software Engineering, Limerick, Ireland,. [4] R. S. Pressman, SOFTWARE ENGINEERING: A Practitioner's Approach, 6th ed. McGraw-Hill, 5. [5] G. G. Roy, "A risk management framework for software engineering practice", Proceedings of the 4 Australian Software Engineering Conference, 4. [6] M. Shepperd, C. Schofield, and B. Kitchenham, "Effort estimation using analogy", Proceedings of the 8th International Conference on Software Engineering,

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