Effort Overruns Research Articles

Perception of complexity in engineering design

AbstractThis paper evaluates perception of complexity in a novel explanatory model that relates product performance and engineering effort. Complexity is an intermediate factor with two facets: it enables desired product performance but also requires effort to achieve. Three causal mechanisms explain how exponential growth bias, excess complexity, and differential perception lead to effort overruns. Secondary data from a human subject experiment validates the existence of perception of complexity as a context‐dependent factor that influences required design effort. A two‐level mixed effects regression model quantifies differences in perception among 40 design groups. Results summarize how perception of complexity may contribute to effort overruns and outline future work to further validate the explanatory model and causal mechanisms.

Effort estimation in large-scale software development: An industrial case study

Context: Software projects frequently incur schedule and budget overruns. Planning and estimation are particularly challenging in large and globally distributed agile projects. While software engineering researchers have been investigating effort estimation for many years to help practitioners to improve their estimation processes, there is little empirical research about effort estimation in large-scale distributed projects involving agile teams.Objective: The objective of this paper is three-fold: i) To identify how effort estimation is carried out in large-scale distributed agile projects; ii) to analyze the accuracy of the effort estimation processes in large-scale distributed agile projects; and iii) to identify and investigate the factors that impact the accuracy of effort estimates in large-scale distributed agile projects.Method: We performed an exploratory longitudinal case study. The data collection was operationalized through archival research and semi-structured interviews.Results: The main findings of the studied case are: 1) A two-stage estimation process, with re-estimation at the analysis stage, improves the accuracy of the effort estimates; 2) underestimation is the dominant trend; 3) less mature teams incur larger effort overruns; 4) requirements with larger size/scope incur larger effort overruns; 5) requirements developed in multi-site settings incur larger effort overruns as compared to requirements developed in a co-located setting; 6) requirements priorities impact the accuracy of the effort estimates.Conclusion: A two-stage effort estimation process can improve effort estimation accuracy and seems to address some of the challenges in large-scale agile software development. To improve effort estimates one needs to consider team maturity, distribution as well as requirements size and priorities.

The influence of selection bias on effort overruns in software development projects

ContextA potentially important, but neglected, reason for effort overruns in software projects is related to selection bias. Selection bias–induced effort overruns occur when proposals are more likely to be accepted and lead to actual projects when based on effort estimates that are too low rather than on realistic estimates or estimates that are too high. The effect of this bias may be particularly important in bidding rounds, but is potentially relevant in all situations where there is effort or cost-based selection between alternatives. ObjectiveTo better understand the relevance and management of selection bias effects in software development contexts. MethodFirst, we present a statistical model illustrating the relation between selection bias in bidding and other contexts and effort overruns. Then, we examine this relation in an experiment with software professionals who estimated and completed a set of development tasks and examine relevant field study evidence. Finally, we use a selection bias scenario to assess awareness of the effect of selection bias among software providers. ResultsThe results from the statistical model and the experiment demonstrated that selection bias is capable of explaining much of the effort overruns. The field evidence was also consistent with a substantial effect of selection bias on effort overruns, although there are alternative explanations for the findings. We found a low awareness of selection bias among the software providers. ConclusionSelection bias is likely to be an important source of effort overruns and should be addressed to reduce problems related to over-optimistic effort estimates.

Efforts Estimation by Use Case Point using Experience Data

Many software efforts estimation models and methods are invented to make efforts estimation accurate. Unfortunately no model or method is suitable for all kind of project and situations. it is frequently suggested that using experience data, estimation models and checklists can increase software effort estimation accuracy. However, there has been limited empirical research on the subject. It was found that in projects where experience data was utilized in the estimation process, they experienced a lesser magnitude of effort overruns. The use of a checklist also appeared to increase estimation accuracy. The utilization of an estimation model did not appear to have a substantial impact [08].This paper is suggesting that use of estimation model can also produce good estimation results, but historical data is always necessary to assist the estimation. We can use historical data to improve the result of Use Case Point and COCOMO model .In our research we have gain 10% improvement in Use case Point model with use of historical data. This paper is also suggesting that a strong monitoring policy is always required to make your estimation as a success.

A comparison of software project overruns - flexible versus sequential development models

Flexible software development models, e.g., evolutionary and incremental models, have become increasingly popular. Advocates claim that among the benefits of using these models is reduced overruns, which is one of the main challenges of software project management. This paper describes an in-depth survey of software development projects. The results support the claim that projects which employ a flexible development model experience less effort overruns than do those which employ a sequential model. The reason for the difference is not obvious. We found, for example, no variation in project size, estimation process, or delivered proportion of planned functionality between projects applying different types of development model. When the managers were asked to provide reasons for software overruns and/or estimation accuracy, the largest difference was that more of flexible projects than sequential projects cited good requirement specifications-and good collaboration/communication with clients as contributing to accurate estimates.

Impact of effort estimates on software project work

This paper presents results from two case studies and two experiments on how effort estimates impact software project work. The studies indicate that a meaningful interpretation of effort estimation accuracy requires knowledge about the size and nature of the impact of the effort estimates on the software work. For example, we found that projects with high priority on costs and incomplete requirements specifications were prone to adjust the work to fit the estimate when the estimates were too optimistic, while too optimistic estimates led to effort overruns for projects with high priority on quality and well specified requirements. Two hypotheses were derived from the case studies and tested experimentally. The experiments indicate that: (1) effort estimates can be strongly impacted by anchor values, e.g. early indications on the required effort. This impact is present even when the estimators are told that the anchor values are irrelevant as estimation information; (2) too optimistic effort estimates lead to less use of effort and more errors compared with more realistic effort estimates on programming tasks.