Software Engineering: A Practitioner’s Approach Chapter 25 Process and Project Metrics copyright © 1996, 2001, 2005 R.S. Pressman & Associates, Inc. For University Use Only May be reproduced ONLY for student use at the university level when used in conjunction with Software Engineering: A Practitioner's Approach. Any other reproduction or use is expressly prohibited.

Until you can measure something and express it in numbers, you have only the beginning of understanding. - Lord Kelvin

Until you can measure something and express it in numbers, you have only the beginning of understanding. - Lord Kelvin Non-software metrics you use everyday: - Gas tank scale - Speedometer - Thermostat in your house - Battery monitor in your laptop What would happen if instead these were not numeric? What other examples do you have? Gas Tank ☐A Lot ☐Some A little

Until you can measure something and express it in numbers, you have only the beginning of understanding. - Lord Kelvin The problem is non-numeric measurements are subjective… they mean different things to different people. Numbers are objective… they mean the same thing to everyone! When your friend says “oh yeah, John/Jane Doe is super attractive, you should go out with him/her”… that is a subjective measurement… so, you ask “Send me a photo”. Why? Because the subjective term “super attractive” has vastly different meanings for different people! Coming up: Metrics for software

Metrics for software When asked to measure something, always try to determine an objective measurement. If not possible, try to get as close as you can! Coming up: A Good Manager Measures

A Good Manager Measures process process metrics project metrics measurement product metrics product What do we use as a basis? • size? product measurements are things like code complexity, number of function, classes, coupling, etc… process/project metrics are how well your process works • function? Coming up: We need a basis to say 20 defects per X lines of code. Why is this important?

We need a basis to say 20 defects per X lines of code We need a basis to say 20 defects per X lines of code. Why is this important? A Because lines of code equals cost B We want our metrics to be valid across projects of many sizes C Because you just caused me to die in God of War III… stop asking these questions! D Because this helps up understand how big our program is Coming up: Why Do We Measure?

Why Do We Measure? assess the status of an ongoing project track potential risks uncover problem areas before they go “critical,” adjust work flow or tasks, evaluate the project team’s ability to control quality of software work products. Coming up: Process versus Project Metrics

Process versus Project Metrics Process Metrics - Measure the process to help update and change the process as needed across many projects Project Metrics - Measure specific aspects of a single project to improve the decisions made on that project Process may aggregate % delay of deliverables (schedule conformance) across many projects to determine how good our scheduling/planning process is Project would use the same measurement to make project level decisions Frequently the same measurements can be used for both purposes Coming up: Process Measurement

Process Measurement We measure the efficacy of a software process indirectly. That is, we derive a set of metrics based on the outcomes of the process Outcomes include measures of errors uncovered before release of the software defects delivered to and reported by end-users work products delivered (productivity) human effort expended calendar time expended schedule conformance many others… We also derive process metrics by measuring the characteristics of specific software engineering tasks. Efficacy - does the process do what is intended Specific tasks - measure any of the above for a specific phase (communication or analysis, design, construction) Coming up: Process Metrics Guidelines

Process Metrics Guidelines Use common sense and organizational sensitivity when interpreting metrics data. Provide regular feedback to the individuals and teams who collect measures and metrics. Don’t use metrics to appraise individuals. Work with practitioners and teams to set clear goals and metrics that will be used to achieve them. Never use metrics to threaten individuals or teams. Metrics data that indicate a problem area should not be considered “negative.” These data are merely an indicator for process improvement. Don’t obsess on a single metric to the exclusion of other important metrics. Coming up: If I calculate the number of defects per developer and rank them, then using that rank assign salary raises based on that.

If I calculate the number of defects per developer and rank them, then using that rank assign salary raises based on that. A. This is good B. This is bad Coming up: Software Process Improvement

Software Process Improvement Process model SPI Process improvement recommendations Improvement goals Process metrics Make your metrics actionable! Coming up: Typical Process Metrics

Typical Process Metrics Quality-related focus on quality of work products and deliverables Productivity-related Production of work-products related to effort expended Statistical SQA data error categorization & analysis Defect removal efficiency propagation of errors from process activity to activity Reuse data The number of components produced and their degree of reusability Within a single project this can also be a “project metric”. Across projects this is a “process metric”. Correctness Maintainability Integrity Usability Earned Value Analysis Defects found in this stage --------------------------------------- This Stage + Next Stage Severity of errors (1-5) MTTF (Mean time to failure) MTTR (Mean time to repair) SQA - types of errors (0-5), MTTF (failure), MTTR (Repair), Quality - Correctness-adherence to rqmts, Maintainability-easy to fix?, Integrity-attack vulnerability, Usability-training time, number of screens, etc… Coming up: Can you calculate a metric that records the number of ‘e’ that appear in a program? A. Yes B. No

Can you calculate a metric that records the number of ‘e’ that appear in a program? A. Yes B. No Should you calculate the number of ‘e’ in a program? A. Yes B. No Coming up: Effective Metrics (ch 16)

Effective Metrics (ch 16) Simple and computable Empirically and intuitively persuasive Consistent and objective Consistent in use of units and dimensions Programming language independent Should be actionable Persuasive - Be what you would naturally think about a metric Coming up: Effective Metrics- Baseball On Base Percentage

Effective Metrics- Baseball On Base Percentage Simple and computable Empirically and intuitively persuasive Consistent and objective Consistent in use of units and dimensions Programming language independent Should be actionable Not effective for “general audience” Persuasive - Be what you would naturally think about a metric Coming up: Effective Metrics- Baseball Runs Batted In

Effective Metrics- Baseball Runs Batted In Count of times when the outcome of player’s at-bat results in a run being scored Simple and computable Empirically and intuitively persuasive Consistent and objective Consistent in use of units and dimensions Programming language independent Should be actionable Much more effective for “general audience” Persuasive - Be what you would naturally think about a metric Coming up: Actionable Metrics

Actionable Metrics Actionable metrics (or information in general) are metrics that guide change or decisions about something Actionable: Measure the amount of human effort versus use cases completed. Too high: more training, more design, etc… Very low: maybe we can shorten the schedule Not-Actionable: Measure the number of times the letter “e” appears in code Think before you measure. Don’t waste people’s time! Coming up: Project Metrics

Project Metrics used to minimize the development schedule by making the adjustments necessary to avoid delays and mitigate potential problems and risks used to assess product quality on an ongoing basis and, when necessary, modify the technical approach to improve quality. every project should measure: Inputs —measures of the resources (e.g., people, tools) required to do the work. Outputs —measures of the deliverables or work products created during the software engineering process. Results —measures that indicate the effectiveness of the deliverables. Coming up: Typical Project Metrics

Typical Project Metrics Effort/time per software engineering task Errors uncovered per review hour Scheduled vs. actual milestone dates Changes (number) and their characteristics Distribution of effort on software engineering tasks Dist of effort on SWE == Effort on each phase in the lifecycle (then see where you have problems and correlate them) Actionable: What do you do if it’s too high/low? Coming up: Metrics Guidelines

Metrics Guidelines Same as process metrics guidelines Use common sense and organizational sensitivity when interpreting metrics data. Provide regular feedback to the individuals and teams who have worked to collect measures and metrics. Don’t use metrics to appraise individuals. Work with practitioners and teams to set clear goals and metrics that will be used to achieve them. Never use metrics to threaten individuals or teams. Metrics data that indicate a problem area should not be considered “negative.” These data are merely an indicator for process improvement. Don’t obsess on a single metric to the exclusion of other important metrics. Same as process metrics guidelines Coming up: Typical Size-Oriented Metrics

Typical Size-Oriented Metrics errors per KLOC (thousand lines of code) defects per KLOC $ per LOC pages of documentation per KLOC errors per person-month Errors per review hour LOC per person-month $ per page of documentation TTH Class got here. Coming up: Typical Function-Oriented Metrics

Typical Function-Oriented Metrics errors per Function Point (FP) defects per FP $ per FP pages of documentation per FP FP per person-month Coming up: But.. What is a Function Point?

But.. What is a Function Point? See Book section 23.2.1 for more detail Function points (FP) are a unit measure for software size developed at IBM in 1979 by Richard Albrecht To determine your number of FPs, you classify a system’s features into five classes: Transactions - External Inputs, External Outputs, External Inquires Data storage - Internal Logical Files and External Interface Files Each class is then weighted by complexity as low/average/high Multiplied by a value adjustment factor (determined by asking questions based on 14 system characteristics EI - Info coming into the system EO - provides derived info out of a system (use ILF and EIF to create information) ExtInq - provides non-derived information out of the system (echoes back EI) ILF - Think structures in RAM, datafiles ONLY updated based on EI ExtIF - Think database, datafile updated by anything Coming up: But.. What is a Function Point?

But.. What is a Function Point? Count Low Average High Total External Input x3 x4 x6 External Output x5 x7 External Inquiries Internal Logic Files x10 x15 External Interface Files Be wary of statistics! Unadjusted Total: Value Adjustment Factor: Total Adjusted Value: Coming up: Function Point Categories

Function Point Categories External Inputs (EI) - Info coming into the system External Outputs (EO) - provides derived info out of a system (use ILF and EIF to create information) External Inquiries - provides non-derived information out of the system (echoes back EI) Internal Logical Files (ILF) - Think structures in RAM, datafiles ONLY updated based on EI External Files - Think database, datafile updated by this code, but also other systems Coming up: Function Point Example

Function Point Example http://www.his.sunderland.ac.uk/~cs0mel/Alb_Example.doc Coming up: Comparing LOC and FP

Comparing LOC and FP Representative values developed by QSM Coming up: At IBM in the 70s or 80s (I don’t remember) they paid people per line-of-code they wrote

At IBM in the 70s or 80s (I don’t remember) they paid people per line-of-code they wrote What happened? A. The best programmers got paid the most B. The worst programmers got paid the most C. The sneakiest programmers, got paid the most D. The lawyers got paid the most Coming up: Why opt against LOC?

Why opt against LOC? LOC is not programming language independent LOC cannot use readily countable characteristics that are determined early in the software process LOC “penalizes” inventive (short) implementations that use fewer LOC that other more clumsy versions Other metrics makes it easier to measure the impact of reusable components (screen, widgets, etc…) Other options: COCOMO, Planning Poker, SLIM, Story Points (remember Scrum?), many others… Coming up: Object-Oriented Metrics

Object-Oriented Metrics Number of scenario scripts (use-cases) Number of support classes (required to implement the system but are not immediately related to the problem domain) Average number of support classes per key class (analysis class) Number of subsystems (an aggregation of classes that support a function that is visible to the end-user of a system) Coming up: WebEngineering Project Metrics

WebEngineering Project Metrics Number of static Web pages (the end-user has no control over the content displayed on the page) Number of dynamic Web pages (end-user actions result in customized content displayed on the page) Number of internal page links (internal page links are pointers that provide a hyperlink to some other Web page within the WebApp) Number of persistent data objects Number of external systems interfaced Number of static content objects Number of dynamic content objects Number of executable functions Got here April 13, 2010 Coming up: Measuring Quality

Measuring Quality Correctness — the degree to which a program operates according to specification Maintainability—the degree to which a program is amenable to change Integrity—the degree to which a program is impervious to outside attack Usability—the degree to which a program is easy to use Verified non-conformance with reqmts ---------------------------------- KLOC MTTC Mean time to change: time to analyze, design, implement and deploy a change t=threat probability s=security = likelihood of repelling attack Integrity =  1-(threat*(1-security)) E.g. t=0.25, s=0.95 --> I=0.99 Correctness - number of Maintainability - MTTC (meant time to change) - given an incoming change req, what is time to design, implement and test the change Integrity – T=Liklihood of threat occuring, S = likelihood of repelling the attack Many options. See ch 12 Coming up: Defect Removal Efficiency

Defect Removal Efficiency DRE = E /(E + D) E is the number of errors found before delivery of the software to the end-user D is the number of defects found after delivery. Provides a measure of how well your team removed defects during a phase. If that DRE is 33%, you found 33% of the defects that you should have found. Coming up: Defect Removal Efficiency

Defect Removal Efficiency DRE = E /(E + D) Defects found during phase: Requirements (10) Design (20) Construction Implementation (5) Unit Testing (50) Testing Integration Testing (100) System Testing (250) Acceptance Testing (5) By Customer (10) 10 / (10 + 20) = 33% What are the rest? 10 / (10 + 20) = 33% 20 / (20 + 50) = 28% 5 / (5 + 50) = 9% 50 / (50 + 100) = 33% 100 / (100 + 250) = 28% 250 / (250 + 5) = 98% 5 / (5 + 10) = 33% Coming up: Metrics for Small Organizations

Metrics for Small Organizations time (hours or days) elapsed from the time a request is made until evaluation is complete, tqueue. effort (person-hours) to perform the evaluation, Weval. time (hours or days) elapsed from completion of evaluation to assignment of change order to personnel, teval. effort (person-hours) required to make the change, Wchange. time required (hours or days) to make the change, tchange. errors uncovered during work to make change, Echange. defects uncovered after change is released to the customer base, Dchange. Coming up: Establishing a Metrics Program

Establishing a Metrics Program Set Goals Identify your business goals. Identify what you want to know or learn. Identify your subgoals. Identify the entities and attributes related to your subgoals. Formalize your measurement goals. Determine indicators for goals Identify quantifiable questions and the related indicators that you will use to help you achieve your measurement goals. Identify the data elements that you will collect to construct the indicators that help answer your questions. Define Measurements Define the measures to be used, and make these definitions operational. Identify the actions that you will take to implement the measures. Prepare a plan for implementing the measures. Coming up: Metrics give you information!

Metrics give you information! Metrics about your process help you determine if you need to make changes or if your process is working Metrics about your project do they same thing Metrics about your software can help you understand it better, and see where possible problems may lurk. Let’s see the complexity measurement (after a few questions…) Coming up: Questions

Questions What are some reasons NOT to use lines of code to measure size? What do you expect the DRE rate will be for the implementation (or construction) phase of the software lifecycle? What about for testing? Give an example of a usability metric? According to the chart, Smalltalk is much more efficient than Java and C++. Why don’t we use it for everything? Coming up: References

Code Metrics Previously we have been discussing product and process metrics. However, code metrics are used to make your code better Complexity - See CyclomaticComplexity slides Dynamic measurements – Profile Jkaboom Memory – garbage collector, object crerations CPU performance Threading Coming up: Questions

References http://www.lansing.lib.il.us/images/baseball_player.gif End of presentation