1,151,321 research outputs found
Software Project Duration Estimation Using Metrix Model
This paper analyzes the existing types of duration estimation models for software projects and advances a new, user-friendly model for project duration estimation. The Metrix model is a stochastic model for software project duration estimation using Monte Carlo simulation over an activity graph. The first advantage of using the Metrix model is that it produces a probability distribution of the software project duration and not a single estimate for duration. Further to this approach, we diminish the project uncertainty by giving the manager better control over the project duration and the associated probability of a certain duration outcome. The second advantage of the Metrix model is that for Monte Carlo simulation it relies upon the historic duration estimation of the team members and not on probability distribution functions which are rather difficult to come with. JEL classification: L86 computer software, O22 project analysis.Software project duration estimation, Monte Carlo simulation, Metrix model.
Evaluating prediction systems in software project estimation
This is the Pre-print version of the Article - Copyright @ 2012 ElsevierContext: Software engineering has a problem in that when we empirically evaluate competing prediction systems we obtain conflicting results.
Objective: To reduce the inconsistency amongst validation study results and provide a more formal foundation to interpret results with a particular focus on continuous prediction systems.
Method: A new framework is proposed for evaluating competing prediction systems based upon (1) an unbiased statistic, Standardised Accuracy, (2) testing the result likelihood relative to the baseline technique of random ‘predictions’, that is guessing, and (3) calculation of effect sizes.
Results: Previously published empirical evaluations of prediction systems are re-examined and the original conclusions shown to be unsafe. Additionally, even the strongest results are shown to have no more than a medium effect size relative to random guessing.
Conclusions: Biased accuracy statistics such as MMRE are deprecated. By contrast this new empirical validation framework leads to meaningful results. Such steps will assist in performing future meta-analyses and in providing more robust and usable recommendations to practitioners.Martin Shepperd was supported by the UK Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/H050329
Application of expert systems in project management decision aiding
The feasibility of developing an expert systems-based project management decision aid to enhance the performance of NASA project managers was assessed. The research effort included extensive literature reviews in the areas of project management, project management decision aiding, expert systems technology, and human-computer interface engineering. Literature reviews were augmented by focused interviews with NASA managers. Time estimation for project scheduling was identified as the target activity for decision augmentation, and a design was developed for an Integrated NASA System for Intelligent Time Estimation (INSITE). The proposed INSITE design was judged feasible with a low level of risk. A partial proof-of-concept experiment was performed and was successful. Specific conclusions drawn from the research and analyses are included. The INSITE concept is potentially applicable in any management sphere, commercial or government, where time estimation is required for project scheduling. As project scheduling is a nearly universal management activity, the range of possibilities is considerable. The INSITE concept also holds potential for enhancing other management tasks, especially in areas such as cost estimation, where estimation-by-analogy is already a proven method
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