Home > Articles > Software Development & Management

  • Print
  • + Share This
This chapter is from the book

4.4 Examples of Metrics Programs

4.4.1 Motorola

Motorola's software metrics program is well articulated by Daskalantonakis (1992). By following the Goal/Question/Metric paradigm of Basili and Weiss (1984), goals were identified, questions were formulated in quantifiable terms, and metrics were established. The goals and measurement areas identified by the Motorola Quality Policy for Software Development (QPSD) are listed in the following.

Goals

  • Goal 1: Improve project planning.

  • Goal 2: Increase defect containment.

  • Goal 3: Increase software reliability.

  • Goal 4: Decrease software defect density.

  • Goal 5: Improve customer service.

  • Goal 6: Reduce the cost of nonconformance.

  • Goal 7: Increase software productivity.

Measurement Areas

  • Delivered defects and delivered defects per size

  • Total effectiveness throughout the process

  • Adherence to schedule

  • Accuracy of estimates

  • Number of open customer problems

  • Time that problems remain open

  • Cost of nonconformance

  • Software reliability

For each goal the questions to be asked and the corresponding metrics were also formulated. In the following, we list the questions and metrics for each goal:1

Goal 1: Improve Project Planning

Question 1.1: What was the accuracy of estimating the actual value of project schedule?

Metric 1.1 : Schedule Estimation Accuracy (SEA)

Question 1.2: What was the accuracy of estimating the actual value of project effort?

Metric 1.2 : Effort Estimation Accuracy (EEA)

Goal 2: Increase Defect Containment

Question 2.1: What is the currently known effectiveness of the defect detection process prior to release?

Metric 2.1: Total Defect Containment Effectiveness (TDCE)

Question 2.2: What is the currently known containment effectiveness of faults introduced during each constructive phase of software development for a particular software product?

Metric 2.2: Phase Containment Effectiveness for phase i (PCEi)

NOTE

From Daskalantonakis's definition of error and defect, it appears that Motorola's use of the two terms differs from what was discussed earlier in this chapter. To understand the preceding metric, consider Daskalantonakis's definitions:

  • Error: A problem found during the review of the phase where it was introduced.

  • Defect: A problem found later than the review of the phase where it was introduced.

  • Fault: Both errors and defects are considered faults.

Goal 3: Increase Software Reliability

Question 3.1: What is the rate of software failures, and how does it change over time?

Metric 3.1: Failure Rate (FR)

Goal 4: Decrease Software Defect Density

Question 4.1: What is the normalized number of in-process faults, and how does it compare with the number of in-process defects?

Metric 4.1a: In-process Faults (IPF)

Metric 4.1b: In-process Defects (IPD)

Question 4.2: What is the currently known defect content of software delivered to customers, normalized by Assembly-equivalent size?

Metric 4.2a: Total Released Defects (TRD) total

Metric 4.2b: Total Released Defects (TRD) delta

Question 4.3: What is the currently known customer-found defect content of software delivered to customers, normalized by Assembly-equivalent source size?

Metric 4.3a: Customer-Found Defects (CFD) total

Metric 4.3b: Customer-Found Defects (CFD) delta

Goal 5: Improve Customer Service

Question 5.1 What is the number of new problems opened during the month? Metric 5.1: New Open Problems (NOP)

NOP = Total new postrelease problems opened during the month

Question 5.2 What is the total number of open problems at the end of the month? Metric 5.2: Total Open Problems (TOP)

TOP = Total postrelease problems that remain open at the end of the month

Question 5.3: What is the mean age of open problems at the end of the month? Metric 5.3: Mean Age of Open Problems (AOP)

AOP = (Total time postrelease problems remaining open at the end of the month have been open)/(Number of open post release problems remaining open at the end of the month)

Question 5.4: What is the mean age of the problems that were closed during the month?

Metric 5.4: Mean Age of Closed Problems (ACP)

ACP = (Total time postrelease problems closed within the month were open)/(Number of open postrelease problems closed within the month)

Goal 6: Reduce the Cost of Nonconformance

Question 6.1: What was the cost to fix postrelease problems during the month? Metric 6.1: Cost of Fixing Problems (CFP)

CFP = Dollar cost associated with fixing postrelease problems within the month

Goal 7: Increase Software Productivity

Question 7.1: What was the productivity of software development projects (based on source size)?

Metric 7.1a: Software Productivity total (SP total)

Metric 7.1b: Software Productivity delta (SP delta)

From the preceding goals one can see that metrics 3.1, 4.2a, 4.2b, 4.3a, and 4.3b are metrics for end-product quality, metrics 5.1 through 5.4 are metrics for software maintenance, and metrics 2.1, 2.2, 4.1a, and 4.1b are in-process quality metrics. The others are for scheduling, estimation, and productivity.

In addition to the preceding metrics, which are defined by the Motorola Software Engineering Process Group (SEPG), Daskalantonakis describes in-process metrics that can be used for schedule, project, and quality control. Without getting into too many details, we list these additional in-process metrics in the following. [For details and other information about Motorola's software metrics program, see Daskalantonakis's original article (1992).] Items 1 through 4 are for project status/control and items 5 through 7 are really in-process quality metrics that can provide information about the status of the project and lead to possible actions for further quality improvement.

  1. Life-cycle phase and schedule tracking metric: Track schedule based on life-cycle phase and compare actual to plan.

  2. Cost/earned value tracking metric: Track actual cumulative cost of the project versus budgeted cost, and actual cost of the project so far, with continuous update throughout the project.

  3. Requirements tracking metric: Track the number of requirements change at the project level.

  4. Design tracking metric: Track the number of requirements implemented in design versus the number of requirements written.

  5. Fault-type tracking metric: Track causes of faults.

  6. Remaining defect metrics: Track faults per month for the project and use Rayleigh curve to project the number of faults in the months ahead during development.

  7. Review effectiveness metric: Track error density by stages of review and use control chart methods to flag the exceptionally high or low data points.

4.4.2 Hewlett-Packard

Grady and Caswell (1986) offer a good description of Hewlett-Packard's software metric program, including both the primitive metrics and computed metrics that are widely used at HP. Primitive metrics are those that are directly measurable and accountable such as control token, data token, defect, total operands, LOC, and so forth. Computed metrics are metrics that are mathematical combinations of two or more primitive metrics. The following is an excerpt of HP's computed metrics:2

Average fixed defects/working day: self-explanatory.

Average engineering hours/fixed defect: self-explanatory.

Average reported defects/working day: self-explanatory.

Bang: "A quantitative indicator of net usable function from the user's point of view" (DeMarco, 1982). There are two methods for computing Bang. Computation of Bang for function-strong systems involves counting the tokens entering and leaving the function multiplied by the weight of the function. For data-strong systems it involves counting the objects in the database weighted by the number of relationships of which the object is a member.

Branches covered/total branches: When running a program, this metric indicates what percentage of the decision points were actually executed.

Defects/KNCSS: Self-explanatory (KNCSS—Thousand noncomment source statements).

Defects/LOD: Self-explanatory (LOD—Lines of documentation not included in program source code).

Defects/testing time: Self-explanatory.

Design weight: "Design weight is a simple sum of the module weights over the set of all modules in the design" (DeMarco, 1982). Each module weight is a function of the token count associated with the module and the expected number of decision counts which are based on the structure of data.

NCSS/engineering month: Self-explanatory.

Percent overtime: Average overtime/40 hours per week.

Phase: engineering months/total engineering months: Self-explanatory.

Of these metrics, defects/KNCSS and defects/LOD are end-product quality metrics. Defects/testing time is a statement of testing effectiveness, and branches covered/ total branches is testing coverage in terms of decision points. Therefore, both are meaningful in-process quality metrics. Bang is a measurement of functions and NCSS/engineering month is a productivity measure. Design weight is an interesting measurement but its use is not clear. The other metrics are for workload, schedule, project control, and cost of defects.

As Grady and Caswell point out, this list represents the most widely used computed metrics at HP, but it may not be comprehensive. For instance, many others are discussed in other sections of their book. For example, customer satisfaction measurements in relation to software quality attributes are a key area in HP's software metrics. As mentioned earlier in this chapter, the software quality attributes defined by HP are called FURPS (functionality, usability, reliability, performance, and supportability). Goals and objectives for FURPS are set for software projects. Furthermore, to achieve the FURPS goals of the end product, measurable objectives using FURPS for each life-cycle phase are also set (Grady and Caswell, 1986, pp. 159–162).

MacLeod (1993) describes the implementation and sustenance of a software inspection program in an HP division. The metrics used include average hours per inspection, average defects per inspection, average hours per defect, and defect causes. These inspection metrics, used appropriately in the proper context (e.g., comparing the current project with previous projects), can be used to monitor the inspection phase (front end) of the software development process.

4.4.3 IBM Rochester

Because many examples of the metrics used at IBM Rochester have already been discussed or will be elaborated on later, here we give just an overview. Furthermore, we list only selected quality metrics; metrics related to project management, productivity, scheduling, costs, and resources are not included.

  • Overall customer satisfaction as well as satisfaction with various quality attributes such as CUPRIMDS (capability, usability, performance, reliability, install, maintenance, documentation/information, and service).

  • Postrelease defect rates such as those discussed in section 4.1.1.

  • Customer problem calls per month

  • Fix response time

  • Number of defective fixes

  • Backlog management index

  • Postrelease arrival patterns for defects and problems (both defects and non-defect-oriented problems)

  • Defect removal model for the software development process

  • Phase effectiveness (for each phase of inspection and testing)

  • Inspection coverage and effort

  • Compile failures and build/integration defects

  • Weekly defect arrivals and backlog during testing

  • Defect severity

  • Defect cause and problem component analysis

  • Reliability: mean time to initial program loading (IPL) during testing

  • Stress level of the system during testing as measured in level of CPU use in terms of number of CPU hours per system per day during stress testing

  • Number of system crashes and hangs during stress testing and system testing

  • Models for postrelease defect estimation

  • Various customer feedback metrics at the end of the development cycle before the product is shipped

  • S curves for project progress comparing actual to plan for each phase of development such as number of inspections conducted by week, LOC integrated by week, number of test cases attempted and succeeded by week, and so forth.

  • + Share This
  • 🔖 Save To Your Account

InformIT Promotional Mailings & Special Offers

I would like to receive exclusive offers and hear about products from InformIT and its family of brands. I can unsubscribe at any time.

Overview


Pearson Education, Inc., 221 River Street, Hoboken, New Jersey 07030, (Pearson) presents this site to provide information about products and services that can be purchased through this site.

This privacy notice provides an overview of our commitment to privacy and describes how we collect, protect, use and share personal information collected through this site. Please note that other Pearson websites and online products and services have their own separate privacy policies.

Collection and Use of Information


To conduct business and deliver products and services, Pearson collects and uses personal information in several ways in connection with this site, including:

Questions and Inquiries

For inquiries and questions, we collect the inquiry or question, together with name, contact details (email address, phone number and mailing address) and any other additional information voluntarily submitted to us through a Contact Us form or an email. We use this information to address the inquiry and respond to the question.

Online Store

For orders and purchases placed through our online store on this site, we collect order details, name, institution name and address (if applicable), email address, phone number, shipping and billing addresses, credit/debit card information, shipping options and any instructions. We use this information to complete transactions, fulfill orders, communicate with individuals placing orders or visiting the online store, and for related purposes.

Surveys

Pearson may offer opportunities to provide feedback or participate in surveys, including surveys evaluating Pearson products, services or sites. Participation is voluntary. Pearson collects information requested in the survey questions and uses the information to evaluate, support, maintain and improve products, services or sites, develop new products and services, conduct educational research and for other purposes specified in the survey.

Contests and Drawings

Occasionally, we may sponsor a contest or drawing. Participation is optional. Pearson collects name, contact information and other information specified on the entry form for the contest or drawing to conduct the contest or drawing. Pearson may collect additional personal information from the winners of a contest or drawing in order to award the prize and for tax reporting purposes, as required by law.

Newsletters

If you have elected to receive email newsletters or promotional mailings and special offers but want to unsubscribe, simply email information@informit.com.

Service Announcements

On rare occasions it is necessary to send out a strictly service related announcement. For instance, if our service is temporarily suspended for maintenance we might send users an email. Generally, users may not opt-out of these communications, though they can deactivate their account information. However, these communications are not promotional in nature.

Customer Service

We communicate with users on a regular basis to provide requested services and in regard to issues relating to their account we reply via email or phone in accordance with the users' wishes when a user submits their information through our Contact Us form.

Other Collection and Use of Information


Application and System Logs

Pearson automatically collects log data to help ensure the delivery, availability and security of this site. Log data may include technical information about how a user or visitor connected to this site, such as browser type, type of computer/device, operating system, internet service provider and IP address. We use this information for support purposes and to monitor the health of the site, identify problems, improve service, detect unauthorized access and fraudulent activity, prevent and respond to security incidents and appropriately scale computing resources.

Web Analytics

Pearson may use third party web trend analytical services, including Google Analytics, to collect visitor information, such as IP addresses, browser types, referring pages, pages visited and time spent on a particular site. While these analytical services collect and report information on an anonymous basis, they may use cookies to gather web trend information. The information gathered may enable Pearson (but not the third party web trend services) to link information with application and system log data. Pearson uses this information for system administration and to identify problems, improve service, detect unauthorized access and fraudulent activity, prevent and respond to security incidents, appropriately scale computing resources and otherwise support and deliver this site and its services.

Cookies and Related Technologies

This site uses cookies and similar technologies to personalize content, measure traffic patterns, control security, track use and access of information on this site, and provide interest-based messages and advertising. Users can manage and block the use of cookies through their browser. Disabling or blocking certain cookies may limit the functionality of this site.

Do Not Track

This site currently does not respond to Do Not Track signals.

Security


Pearson uses appropriate physical, administrative and technical security measures to protect personal information from unauthorized access, use and disclosure.

Children


This site is not directed to children under the age of 13.

Marketing


Pearson may send or direct marketing communications to users, provided that

  • Pearson will not use personal information collected or processed as a K-12 school service provider for the purpose of directed or targeted advertising.
  • Such marketing is consistent with applicable law and Pearson's legal obligations.
  • Pearson will not knowingly direct or send marketing communications to an individual who has expressed a preference not to receive marketing.
  • Where required by applicable law, express or implied consent to marketing exists and has not been withdrawn.

Pearson may provide personal information to a third party service provider on a restricted basis to provide marketing solely on behalf of Pearson or an affiliate or customer for whom Pearson is a service provider. Marketing preferences may be changed at any time.

Correcting/Updating Personal Information


If a user's personally identifiable information changes (such as your postal address or email address), we provide a way to correct or update that user's personal data provided to us. This can be done on the Account page. If a user no longer desires our service and desires to delete his or her account, please contact us at customer-service@informit.com and we will process the deletion of a user's account.

Choice/Opt-out


Users can always make an informed choice as to whether they should proceed with certain services offered by InformIT. If you choose to remove yourself from our mailing list(s) simply visit the following page and uncheck any communication you no longer want to receive: www.informit.com/u.aspx.

Sale of Personal Information


Pearson does not rent or sell personal information in exchange for any payment of money.

While Pearson does not sell personal information, as defined in Nevada law, Nevada residents may email a request for no sale of their personal information to NevadaDesignatedRequest@pearson.com.

Supplemental Privacy Statement for California Residents


California residents should read our Supplemental privacy statement for California residents in conjunction with this Privacy Notice. The Supplemental privacy statement for California residents explains Pearson's commitment to comply with California law and applies to personal information of California residents collected in connection with this site and the Services.

Sharing and Disclosure


Pearson may disclose personal information, as follows:

  • As required by law.
  • With the consent of the individual (or their parent, if the individual is a minor)
  • In response to a subpoena, court order or legal process, to the extent permitted or required by law
  • To protect the security and safety of individuals, data, assets and systems, consistent with applicable law
  • In connection the sale, joint venture or other transfer of some or all of its company or assets, subject to the provisions of this Privacy Notice
  • To investigate or address actual or suspected fraud or other illegal activities
  • To exercise its legal rights, including enforcement of the Terms of Use for this site or another contract
  • To affiliated Pearson companies and other companies and organizations who perform work for Pearson and are obligated to protect the privacy of personal information consistent with this Privacy Notice
  • To a school, organization, company or government agency, where Pearson collects or processes the personal information in a school setting or on behalf of such organization, company or government agency.

Links


This web site contains links to other sites. Please be aware that we are not responsible for the privacy practices of such other sites. We encourage our users to be aware when they leave our site and to read the privacy statements of each and every web site that collects Personal Information. This privacy statement applies solely to information collected by this web site.

Requests and Contact


Please contact us about this Privacy Notice or if you have any requests or questions relating to the privacy of your personal information.

Changes to this Privacy Notice


We may revise this Privacy Notice through an updated posting. We will identify the effective date of the revision in the posting. Often, updates are made to provide greater clarity or to comply with changes in regulatory requirements. If the updates involve material changes to the collection, protection, use or disclosure of Personal Information, Pearson will provide notice of the change through a conspicuous notice on this site or other appropriate way. Continued use of the site after the effective date of a posted revision evidences acceptance. Please contact us if you have questions or concerns about the Privacy Notice or any objection to any revisions.

Last Update: November 17, 2020