Core risk in software projects: incorrectly determined time and effort

Erroneous estimates of time and effort are a major risk in software projects. No wonder Tom DeMarco and Timothy Lister list this in the book “Bärentango - Leading projects to success with risk management” (note for translation, in English: Waltzing with Bears: Managing Risk on Software Projects) as the first point in their 5 core risks. But why are so many projects misjudged?

There are several reasons for this:

1. When identifying the necessary activities in the project structure plan, it is more likely that work steps that later prove necessary will be forgotten than that work that later proves unnecessary will be included.
2. Initial estimates are made at a very early stage with little in-depth knowledge and low quality requirements and are then no longer updated despite new findings.
3. Estimates are only made by individuals, are made “from the gut” and are not scrutinized in a sufficiently well-founded manner.
4. Especially in projects with a lot of management attention, the high expectations of stakeholders can influence the estimation process towards wishful thinking. This is particularly the case when the estimation process is poorly carried out and documented, making it barely comprehensible.
5. The final estimate chosen from the confidence interval of possible results is selected unsystematically or with a tendency towards optimism.
6. There is no established feedback loop with sufficiently detailed resolution to calibrate and thus improve the estimation process based on real experience in terms of continuous improvement management for the future.

In view of the many influencing factors, the holistic solution is not trivial. Because even with sophisticated estimation methods such as COCOMO II For example, the problem persists that the necessary scope is simply underestimated before the algorithm is set in motion. In this way, the estimation error can simply be shifted to another process step. In addition, as the complexity of the process increases, so does the need for training.

In view of the challenge, is it even worthwhile to deal with the topic? Yes, absolutely. Using empirical methods, DeMarco and Lister were able to isolate this risk to such an extent that this risk alone is likely to result in a project delay of over 30% in any average project compared to the original planning. The data shows an imbalance towards smaller projects. For larger projects, the deviations are usually smaller. The fact that, in view of the scale, a more systematic estimation approach is chosen and that there is more chance of readjusting the planning in longer projects is likely to play a role. However, when viewed in absolute terms, fewer percent variances in a much larger volume are no less bad than large deviations in many small projects.

Applying a scientific approach

Many detailed improvements for better estimates are obvious and are well known. Yet many teams are struggling with implementation. However, since the quality of the estimation process is very easy to quantify, a scientific approach is recommended:

1. Work with confidence intervals and refine existing estimates based on new findings.
2. Determine the quality of the estimates and integrate appropriate key performance indicators (KPI) into the processes.
3. Train and calibrate your team to make them better estimators. To do this, establish feedback loops.

Finally a tool for more accurate estimates

With the app Quantum: :Estimation The estimation process can now be supported more thoroughly directly in Jira. By using advanced methods such as PERT and interval estimates, ambiguities can be localized and treated and confidence in the estimation can be increased step by step.

The app is suitable for agile/lean teams or even for distributed teams that work together remotely. Who should appreciate what or has already done something can be individually controlled and monitored.

Solved everything?

True to the well-known motto”A fool with a tool is still a fool.“It's important to note that buying an app for Jira is a single piece of the puzzle, albeit a very helpful one. However, full success can only be achieved if the implementation in the process is holistic and sustainable. Accompanying measures such as training and refining quality management in the area of estimates with appropriate feedback loops are therefore important, which not only allow lump sum considerations that are difficult to localize.

--

linkyard and the app manufacturer Data Rocks maintain a well-established, trusting and intensive collaboration in a variety of joint projects. Accordingly, we are also able to tackle complex challenges together.

‍