CMMI Assessment is the process for evaluating compliance and measuring the effectiveness of Specific Practices (SPs) and Generic Practices (GPs) of Process Areas (PAs) in the CMMI Framework. Every assessment process starts with scope definition and finally leading to assessment and sustenance. These common procedures are well explained in Stages in an assessment/certification process .

In additional to this common procedures, there are certain specific points to be ensured if an organization is seeking for compliance to CMMI model. These are detailed below.



Organization needs to have two separate teams- Functional Area Representative (FAR) Team and Assessment Team. These members are elected from/by the organization.

FAR team is further subdivided to multiple sub teams based on

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TL 9000 is a quality management practice set up by the QuEST Forum in 1998 for telecom industry. TL 9000 is grounded on ISO 9001 with industry specific adders.  It adds specific telecom hardware (H), software (S) and service (V) requirements to the more generic practices of ISO 9001. QuEST forum administrates the TL 9000 certification process ( ). The QuEST forum was originated by a number of telecommunication companies, e.g. Bell Atlantic, BellSouth, Pacific Bell and South Western Bell. QuEST stands for ‘Quality Excellence for Suppliers of Telecommunications Leadership’

TL9000 is defined by two documents.

  • TL 9000 Quality Management System Requirements Handbook
  • TL 9000 Quality Management System Measurements Handbook

There are certain specific points to be ensured if an organization is seeking for compliance to this standard in additional to the common procedures as explained in Stages in an assessment/certification process . The specific points are explained below.

1. Registration Options

The process is the same as other registrations (e.g. ISO 9001) except that the scope of registration includes reference to product categories chosen and adders (H, S, V) involved. The registration options available are Hardware (H), Software (S), and Services (V). An organization can choose all or any combination of the options that apply to it. The registration option determines which of the TL 9000 specific requirements apply to the registration. Once an organization finalizes the registration scope and options, it starts the process of mapping its products to the TL 9000 product categories. TL 9000 has over a hundred product categories covering hardware, software and services.

2. Setting the company up in Quest Forum Portal

The TL 9000 Administrator needs to be contacted to initiate setting the company up in Quest Forum Portal. Any person authenticated by the organization can do the same through the TL 9000 administrator. Once access is gained, using the given Registration ID, a Registration ID Profile is created, and maintained.

3. Data Submission

One of the requirements of the TL 9000 registration is the reporting of the measurements data specific to that registration. The Registration Management System (RMS) facilitates online submission of this data on a monthly basis through its web-based interface in the Quest Forum Portal. Based on the product category, decide which all measurements should be collected against the selected product categories, as per the latest version of the measurement handbook. Start uploading to Quest website against the organization. At least 3 iterations of measurement data are required before starting the final assessment of TL 9000.

4. Assessment Process

Once the process of determining, collecting and analysis of measurement data has been formalized and initiated, and the quality management system has been updated to reflect all the TL 9000 Requirements, the company is ready to go for TL 9000 audit/assessment. The assessor/auditor needs to view the data confirmation reports received from the UTD (University of Dallas, TX) who maintain the RMS. If the assessment is successful, then a TL 9000 Certificate is issued. Information is sent to QuEST, and TL 9000 registration follows. Information is found within the QuEST Forum Portal (which includes access to the RMS) on the QuEST Forum website.

Some natural synergies exist between the generic practices and their related process areas as explained in Evidences supporting implementation of CMMI GPs.

Here the ‘recursive relationships between generic practices and their closely related process areas’ are explained.CMMI GPs-PA

For more information on required evidences for each generic practices, please refer Evidences supporting Implementation of CMMI GPs

Many process areas address institutionalization by supporting the implementation of the generic practices. An example is the Project Planning process area and GP 2.2. To implement this generic practice we need to implement the Project Planning process area, all or in part. In the below table such related process areas ( which are supporting the GPs) as well as required artefacts( which could be the evidences for the implementation of GPs) are shown.

In addition to this normal GP-PA relationship, there are some recursive relationships between generic practices and their closely related process areas. This is explained in How does the Generic Practice recursively rpply to its related Process Area(s)?

CMMI was originated by SEI (Software Engineering Institute), sponsored by US Department of Defence. Later on SEI has transferred CMMI-related products and activities to the CMMI Institute, a 100%-controlled subsidiary of Carnegie Innovations, Carnegie Mellon University’s technology commercialization enterprise.

The below pictures illustrates the evolution of CMMI

Evolution of CMMI 1

  • When the era of computerized information systems started in 1960, there was a significant demand for software development. Even though software industry was growing rapidly, many processes for software development were amateur and project failure was common.

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Sub processes are components of a larger defined process. For example, a typical development process may be defined in terms of sub processes such as requirements development, design, build, review and test. The sub processes themselves may be further decomposed into other sub processes and process elements. Measurable parameters are defined for these sub processes to analyse the performance of the sub processes. These sub processes are further studied to identify the critical sub processes which are influencing the process performance objectives i.e. PPO. Measurable objectives are set for the critical sub process measures also. PPOs are derived fromBusiness Objectives (BOs).

In the above paragraph, there is a linkage established starting from sub process to BOs. In fact in an organization

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Baselines are derived statistically using performance data collected over a period of time. They are indicators of current performance of an organization. Hence proper attention must be paid while deriving baselines as an error can cause even a loss of a business. There are some critical, but common mistakes observed in the baselining process as explained below. Crucial steps must be taken to avoid such mistakes.

Pitfall #1: Inapt parameter for baselining.

Organization must plan and define measures that are tangible indicators of process performance. Baselining does not simply imply gathering and baselining the entire set of data available in the organization. Based on the business objectives, the critical processes of the organization whose performance needs to be analyzed is selected. Then process parameters for monitoring the same are defined, collected data and finally baselining done. There is no harm in collecting and baselining the entire parameters defined in the organization, but why should we waste our time collecting data which won’t be used.

Pitfall #2: Not chronological data.

For baselining with control charts, it is essential that the data to be chronological. Hence during data collection itself, time stamp of the data must be noted.

Pitfall #3: Lack of enough number of data points.

In software industry, often we hear complaints from baselining team regarding the deficiency of data points. And when the question is put on project team, they tell like “we just don’t have time” or “it is too difficult”. In order to derive baselines there needs to be a minimum number of data points, say like 10 or so. Then only, at least all the 4 rules of stability can be applied over the data points. But in a software industry people try to build baselines with 8 or less data points. Then it won’t indicate the correct performance level of the process under investigation. In such cases where number of data points is insufficient, baselining needs to be postponed. Or organization can plan to collect more samples by increasing the frequency of data collection.

Pitfall #4: Being inconsistent.

While collecting as well as baselining data, one must use consistent methods and processes. What is being measured in the post baseline data needs to be same as what was measured in the baseline data collection process.

Pitfall #5: Taking non homogeneous data

Data taken for baselining needs to be of homogenous nature. Otherwise the baselining output won’t give the correct indication of process performance. The data can be categorized based on the qualitative parameters like type of project, complexity of the work, nature of development, programming languages etc. instead of clubbing it altogether and thereby leading to loss the homogeneity

Pitfall #6: Absence of data verification.

Usually it is a common mistake to take data blindly from organizational database and start the baselining process. Essentially, data must be verified to ensure its completeness, correctness and consistency before any statistical processing.

Pitfall #7: Not representative sample.

Processes that permits self-selection by respondents aren’t random samples and often aren’t representative of the target population. In order to have a random, representative sample, it has to be ensured that it’s truly random and representative.

Pitfall #8: Basing the baseline value on assumptions, not real data.

People have a tendency to believe that the collected data follows a normal distribution. Sometimes they don’t even check the normality statistically. Another case is like, even after data is found to be non- normal statistically, people try to make it normal by removing some data points. It is logical to remove one or two points out of 15 to 20 points, if there are some assignable reasons. Other than that it is not a good practice, to simply remove the data points in order to make the distribution normal. It is essential to check the actual distribution of the data before going ahead with baselining. Control charts work on a normal data set only. One can check the distribution of the data visually using histograms or so, and can confirm the distribution statistically using some other tools (there are a plenty of excel addins to check the distribution).

Pitfall #9: Ignoring the past Data if there is no process change.

Suppose in an organization yearly baselining is done. In the start of the year 2013 baselines were derived using data points in the previous year, say 2012. Objectives were set to ‘maintain the current process performance’ and no higher targets. And hence no improvement initiatives were triggered to raise the performance level. Next year, data points in the year 2013 were collected for baselining and it was confirmed statistically that both sets of data were equal (data points in 2012 and those in 2013), may the results from a 2 sample T test. Now which data set is taken by the organization for 2014 baselining? It is a common mistake to ignore the 2012 data and do the baselining with 2013 data points alone. Since both sets of data points were similar and statistically equal, both set must be combined in the chronological order while baselining.

Pitfall #10: Blindly taking p value as 0.05

Null hypothesis is rejected if p value is less than a significant level. In the industry, usually the significant level of P is taken as 0.05. Actually P value is an arbitrary value. Higher the p values means; risk attached with it is increasing as we reject a null hypothesis when it was actually true. (Refer more details of p value in the blog hypothesis test ) And it is up to the organization to decide that significant level.

Pitfall #11: Removing out of turn points when there is no assignable causes

Out of turn points cannot be removed if there are no assignable reasons behind it. If there is no reason for an out of turn point, it implies that data is not stable and one cannot go ahead with baselining.

Pitfall #12: Placing unfeasible values as control limits

Sometimes the control limits derived statistically during baselining process may be unworkable. Say for example a baseline of review effectiveness data (in %) cannot have an upper control limit (UCL) as 120% even though statistically it is correct. Similarly a coding speed baseline cannot have a lower control limits (LCL) as -15 lines of code/hr. All such values are unusable. So an organization needs to have a policy to handle such situations. Say for example, an organization can use 25th and 75th percentiles of the stable data as control limits in such scenario. Or organization can decide to change the LCL/UCL to the minimum/maximum permissible value of that parameter. i.e. organization can change the LCL of coding speed as ‘zero’ instead of a negative value and UCL of review effectiveness as 100% in the above examples.

Pitfall #13: Stating the baseline without contextual information

Stating the context description involves a consistent understanding of the result of the measurement process. Contextual information refers to the additional data related to the environment in which a process is executed. As a part of contextual information, timestamp, context, measurement units etc. are collected.

Pitfall #14: Inapt communication mode.

Nowadays, our computer software supports a wide range of graphs. And people try to use those graphs altogether and finally making real stuffs hidden or complex. One must select the right graph to communicate the processed data. Run charts, pie charts, control charts and bar charts are all good means of communication, but the best fit must be chosen.

Pitfall #15: Not beginning with the end in mind.

One must determine in advance how the processed data is going to be used. This helps to make good choices in what data to be collected (never waste time collecting data which won’t be used), what tool to be used. Also one must plan to measure everything needed to know how the effect of the change is going to be calculated. It is usually too late to go back and correct things if something is left out.