Causes of inaccurate software development cost estimates

Lederer, Albert L.; Prasad, Jayesh

doi:10.1016/0164-1212(94)00092-2

Cited by 76 publications

(106 citation statements)

References 24 publications

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“…Several studies [3,6,7,8,9,10,11] have surveyed the accuracy of effort and schedule estimation. The results of these survey showed that 59%-76% projects were completed over estimated effort and 35%-80% were over schedule.…”

Section: The Accuracy Of Software Cost Estimationmentioning

confidence: 99%

A survey on software cost estimation in the chinese software industry

Wang

et al. 2008

Proceedings of the Second ACM-IEEE International Symposium on Empirical Software Engineering and Measurement

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Although a lot of attention has been paid to software cost estimation since 1960, making accurate effort and schedule estimation is still a challenge. To collect evidence and identify potential areas of improvement in software cost estimation, it is important to investigate the estimation accuracy, the estimation method used, and the factors influencing the adoption of estimation methods in current industry. This paper analyzed 112 projects from the Chinese software project benchmarking dataset and conducted questionnaire survey on 116 organizations to investigate the above information. The paper presents the current situations related to software project estimation in China and provides evidence-based suggestions on how to improve software project estimation. Our survey results suggest, e.g., that large projects were more prone to cost and schedule overruns, that most computing managers and professionals were neither satisfied nor dissatisfied with the project estimation, that very few organizations (15%) used model-based methods, and that the high adoption cost and insignificant benefit after adoption were the main causes for low use of model-based methods.

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Section: The Accuracy Of Software Cost Estimationmentioning

confidence: 99%

A survey on software cost estimation in the chinese software industry

Wang

et al. 2008

Proceedings of the Second ACM-IEEE International Symposium on Empirical Software Engineering and Measurement

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“…In particular, this is the case when people know a great deal about how they plan to solve a task and how much time they have spent on similar tasks in the past. Several studies on software development have shown, perhaps for this reason, that predicting one's own time usage is associated with higher accuracy and with no increase in the level of optimism compared to predictions of other people's work [43,44].…”

Section: Let Other People Make the Prediction?mentioning

confidence: 99%

Time Prediction Methods and Principles

Halkjelsvik

Jørgensen

2018

Time Predictions

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The prominent approach for reducing a problem's complexity is to decompose it into less complex subproblems, solve each of these, and then aggregate the subsolutions into an overall solution. In time prediction contexts, this approach is typically the basis of what has been referred to as the bottom-up method, the activity-based method, or predictions based on a work breakdown structure. Generally, across a range of domains, decomposition has been found to improve judgement quality and increase prediction accuracy [1]. In the domain of time predictions, however, there are also situations in which decomposition leads to overoptimistic and less accurate judgements [2].The time prediction literature has examined two types of decomposition strategies: unpacking, which consists of merely listing or thinking about the subcomponents of a task before predicting the time usage as a whole, and decomposition, which consists of unpacking, predicting the time usage for each unpacked component, and then aggregating the time predictions into a prediction of the total time usage.Unpacking: Unpacking tend to give higher time predictions. For example, if you ask people to list all the persons for whom they must buy Christmas presents, they will tend to predict that they need more time to complete their Christmas shopping compared to those who did not generate such a list. Unpacking strategies may be based either on the self-generation of components, as in the example above, or on reminding the participants of possible subcomponents, for example, by using a checklist for activities to be included. Checklists, or reminders, are usually very effective and easy to implement and may improve the accuracy of time predictions, particularly in situations in which there is a tendency towards overoptimistic time predictions [3]. In one study illustrating how unpacking tends to increase time predictions, participants were instructed to format a document to match a printed, edited version of the same document. When asked to predict how long it would take to format the document without any reminders of the work's components, the participants predicted, on average, eight and a half minutes. When asked to predict how long it would take with reminders of the work's components, such as including italics and special characters (ђ,ǒ, î), the participants predicted, on average, about 13 minutes [4].In many cases, the increase in time predictions from unpacking a task contributes to greater realism [5]. Projects that used checklists when predicting time were, for example, more accurate and less overoptimistic (with predictions, on average, 10% too low) than projects that did not (with predictions, on average, 38% too low) [3]. Although unpacking may generally contribute to more accurate and less overoptimistic time predictions, this may not always be the case. Pointing out obvious components of a task, small and simple components, or components that are part of the last steps of a task may not lead to more accurate time predictions [4].There are also ...

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“…Of the remaining 57% of projects that are completed, the average budget overrun is 87%." Lederer and Prasad [4] report that only one in four systems development projects in a study group completed within cost estimates. Lederer …”

Section: Type Of Issuementioning

confidence: 99%