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Metrics. Sudipto Ghosh CS 406 Fall 99 November 30, 1999. Learning objectives. Software metrics Metrics for various phases Why metrics are needed How to collect metrics How to use metrics. Questions. How big is the program? Huge!! How close are you to finishing? We are almost there!!

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    1. Metrics Sudipto Ghosh CS 406 Fall 99 November 30, 1999

    2. Learning objectives • Software metrics • Metrics for various phases • Why metrics are needed • How to collect metrics • How to use metrics CS 406 Testing

    3. Questions • How big is the program? • Huge!! • How close are you to finishing? • We are almost there!! • Can you, as a manager, make any useful decisions from such subjective information? • Need information like, cost, effort, size of project. CS 406 Testing

    4. Metrics • Quantifiable measures that could be used to measure characteristics of a software system or the software development process • Required in all phases • Required for effective management • Managers need quantifiable information, and not subjective information • Subjective information goes against the fundamental goal of engineering) CS 406 Testing

    5. Kinds of software metrics • Product metrics • quantify characteristics of the product being developed • size, reliability • Process metrics • quantify characteristics of the process being used to develop the software • efficiency of fault detection CS 406 Testing

    6. CMM • Level 4: Managed level • Process measurement performed • Quality and productivity goals set • Continually measured and corrective actions taken • Statistical quality controls in place • Level 5: Optimizing level • Statistical quality and process control in place • Positive feedback loop used for improvement in productivity and quality CS 406 Testing

    7. Issues [1] • Cost of collecting metrics • Automation is less costly than manual method • CASE tool may not be free • Development cost of the tool • Extra execution time for collecting metrics • Interpretation of metrics consumes resources • Validity of metrics • Does the metric really measure what it should? • What exactly should be measured? CS 406 Testing

    8. Issues [2] • Selection of metrics for measurement • Hundreds available and with some cost • Basic metrics • Size (like LOC) • Cost (in $$$) • Duration (months) • Effort (person-months) • Quality (number of faults detected) CS 406 Testing

    9. Selection of metrics • Identify problems from the basic metrics • high fault rates during coding phase • Introduce strategy to correct the problems • To monitor success, collect more detailed metrics • fault rates of individual programmers CS 406 Testing

    10. Utility of metrics • LOC • size of product • take a regular intervals and find out how fast the project is growing • What if # defects per 1000 LOC is high? • Then even if the LOC is high, most of the code has to be thrown away. CS 406 Testing

    11. Applicability of metrics • Throughout the software process, like • effort in person-months • staff turnover • cost • Specific to a phase • LOC • # defects detected per hour of reviewing specifications CS 406 Testing

    12. Metrics: planning • When can we plan the entire software project? • At the very beginning? • After a rapid prototype is made? • After the requirements phase? • After the specifications are ready? • Sometimes there is a need to do it early. CS 406 Testing

    13. Metrics: planning • graph of cost estimate 4 3 Relative range of cost estimate 2 Requirements Specifications Design Implementation Integration Phase during which cost estimation is made CS 406 Testing

    14. Planning: Cost estimation • Client wants to know: • How much will I have to pay? • Problem with • underestimation (possible loss by the developer) • overestimation (client may offer bid to someone else) • Cost • internal (salaries of personnel, overheads) • external (usually cost + profit) CS 406 Testing

    15. Cost estimation • Other factors: • desperate for work - charge less • client may think low cost => low quality, so raise the amount • Too many variables • Human factors • Quality of programmers, experience • What if someone leaves midway • Size of product CS 406 Testing

    16. Planning: Duration estimation • Problem with underestimation • unable to keep to schedule, leading to • loss of credibility • possible penalty clauses • Problem with overestimation • the client may go to other developers • Difficulty because of similar reasons as for cost estimation CS 406 Testing

    17. Metrics: planning - size of product • Units for measurement • LOC = lines of code • KDSI = thousand delivered source instructions • Problems • creation of code is only a part of the total effort • effect of using different languages on LOC • how should one count LOC? • executable lines of code? • data definitions • comments? What are the pros and cons? CS 406 Testing

    18. Problems with lines of code • Problems • More on how to count • Job control language statements? • What if lines are changed or deleted? • What if code is reused? • Not all code is delivered to clients • code may be for tool support • What if you are using a code generator? • Early on, you can only estimate the lines of code. So, the cost estimation is based on another estimated quantity!!! CS 406 Testing

    19. Estimating size of product • FFP metric for cost estimation of medium-scale products • Files, flows and processes (FFP) • File: collection of logically or physically related records that are permanently resident • Flow: a data interface between the product and the environment • Process: functionally defined logical or arithmetic manipulation of data S = #Files + #Flows + #Process, C = d X S CS 406 Testing

    20. Techniques of cost estimation • Take into account the following: • Skill levels of the programmers • Complexity of the project • Size of the project • Familiarity of the development team • Hardware • Availability of CASE tools • Deadline effect CS 406 Testing

    21. Techniques of cost estimation • Expert judgement by analogy • Bottom up approach • Algorithmic cost estimation models • Based on mathematical theories • resource consumption during s/w development obeys a specific distribution • Based on statistics • large number of projects are studied • Hybrid models • mathematical models, statistics and expert judgement CS 406 Testing

    22. COCOMO • COnstructive COst MOdel • Series of three models • Basic - macroestimation model • Intermediate COCOMO • Detailed - microestimation model • Estimates total effort in terms of person-months • Cost of development, management, support tasks included • Secretarial staff not included CS 406 Testing

    23. Intermediate COCOMO • Obtain an initial estimate (nominal estimate) of the development effort from the estimate of KDSI • Nominal effort = a X (KDSI)b person-months a b System Organic Semi-detached Embedded 3.2 3.0 2.8 1.05 1.12 1.20 CS 406 Testing

    24. Kind of systems • Organic • Organization has considerable experience in that area • Requirements are less stringent • Small teams • Simple business systems, data processing sys • Semi-detached • New operating system • Database management system • Complex inventory management system CS 406 Testing

    25. Kind of systems • Embedded • Ambitious, novel projects • Organization has little experience • Stringent requirements for interfacing, reliability • Tight constraints from the environment • Embedded avionics systems, real-time command systems CS 406 Testing

    26. Intermediate COCOMO (contd) • Determine a set of 15 multiplying factors from different attributes (cost driver attributes) of the project • Page 274 of the book • Adjust the effort estimate by multiplying the initial estimate with all the multiplying factors • Also have phase-wise distribution CS 406 Testing

    27. Determining the rating • Module complexity multiplier • Very low: control operations consist of a sequence of constructs of structured programming • Low: Nested operators • Nominal: Intermodule control and decision tables • High: Highly nested operators, compound predicates, stacks and queues • Very high: Reentrant and recursive coding, fixed priority handling CS 406 Testing

    28. COCOMO example • System for office automation • Four major modules • data entry: 0.6 KDSI • data update: 0.6 KDSI • date query: 0.8 KDSI • report generator: 1.0 KDSI • Total: 3.0 KDSI • Category: organic • Initial effort: 3.2 * 31.05 = 10.14 PM • (PM = person-months) CS 406 Testing

    29. COCOMO example (contd) • From the requirements the ratings were assessed: • Complexity High 1.15 • Storage High 1.06 • Experience Low 1.13 • Programmer Capability Low 1.17 • Other ratings are nominal • EAF = 1.15 * 1.06 * 1.13 * 1.17 = 1.61 • Adjusted effort = 1.61 * 10.14 = 16.3 PM CS 406 Testing

    30. Metrics: requirements phase • Number of requirements that change during the rest of the software development process • if a large number changed during specification, design, …, something is wrong in the requirements phase • Metrics for rapid prototyping • Are defect rates, mean-time-to-failure useful? • Knowing how often requirements change? • Knowing number of times features are tried? CS 406 Testing

    31. Metrics: specification phase • Size of specifications document • may predict effort required for subsequent products • What can be counted? • Number of items in the data dictionary • number of files • number of data items • number of processes • Tentative information • a process in a DFD may be broken down later into different modules • a number of processes may constitute one module CS 406 Testing

    32. Metrics: specification phase • Cost • Duration • Effort • Quality • number of faults found during inspection • rate at which faults are found (efficiency of inspection) CS 406 Testing

    33. Metrics: design phase • Number of modules (measure of size of target product) • Fault statistics • Module cohesion • Module coupling • Cyclomatic complexity • Fan-in, fan-out CS 406 Testing

    34. Cyclomatic complexity • Number of binary decisions + 1 • The number of branches in a module • Proposed by McCabe • Lower the value of this number, the better • Only control complexity, no data complexity • For OO, cyclomatic complexity is usually low because methods are mostly small • also, data component is important for OO, but ignored in cyclomatic complexity CS 406 Testing

    35. Architecture design as a directed graph • Fan-in of a module: • number of flows into a module plus the number of global data structures accessed by the module • Fan-out of a module: • number of flows out of the module plus the number of data structures updated by the module • Measure of complexity: • length X (fan-in X fan-out)2 CS 406 Testing

    36. OO design metrics • Assumption: The effort in developing a class is determined by the number of methods. • Hence the overall complexity of a class can be measured as a function of the complexity of its methods. Proposal: Weighted Methods per class (WMC) CS 406 Testing

    37. WMC • Let class C have methods M1, M2, .....Mn. • Let Ci denote the complexity of method • How to measure Ci? CS 406 Testing

    38. WMC validation • Most classes tend to have a small number of methods, are simple, and provide some specific abstraction and operations. • WMC metric has a reasonable correlation with fault-proneness of a class. CS 406 Testing

    39. Depth of inheritance tree • Depth of a class in a class hierarchy determines potential for re-use. Deeper classes have higher potential for re-use. • Inheritance increases coupling... changing classes becomes harder. • Depth of Inheritance (DIT) of class C is the length of the shortest path from the root of the inheritance tree to C. • In case of multiple inheritance DIT is the maximum length of the path from the root to C. CS 406 Testing

    40. DIT evaluation • Basili et al. study,1995. • Chidamber and Kemerer study, 1994. • Most classes tend to be close to the root. • Maximum DIT value found to be 10. • Most classes have DIT=0. • DIT is significant in predicting error proneness of a class. Higher DIT leads to higher error-proneness. CS 406 Testing

    41. Metrics: implementation phase • Intuition: more complex modules are more likely to contain faults • Redesigning complex modules may be cheaper than debugging complex faulty modules • Measures of complexity: • LOC • assume constant probability of fault per LOC • empirical evidence: number of faults related to the size of the product CS 406 Testing

    42. Metrics: implementation phase • McCabe’s cyclomatic complexity • Essentially the number of branches in a module • Number of tests needed for branch coverage of a module • Easily computed • In some cases, good for predicting faults • Validity questioned • Theoretical grounds • Experimentally CS 406 Testing

    43. Metrics: implementation phase • Halstead’s software metrics • Number of distinct operators in the module (+. -. If, goto) • Number of distinct operands • Total number of operators • Total number of operands CS 406 Testing

    44. Metrics: implementation phase • High correlation shown between LOC and other complexity metrics • Complexity metrics provide little improvement over LOC • Problem with Halstead metrics for modern languages • Constructor: is it an operator? Operand? CS 406 Testing

    45. Metrics: implementation and integration phase • Total number of test cases • Number of tests resulting in failure • Fault statistics • Total number of faults • Types of faults • misunderstanding the design • lack of initialization • inconsistent use of variables • Statistical-based testing: • zero-failure technique CS 406 Testing

    46. Zero failure technique • The longer a product is tested without a single failure being observed, the greater the likelihood that the product is free of faults. • Assume that the chance of failure decreases exponentially as testing proceeds. • Figure out the number of test hours required without a single failure occurring. CS 406 Testing

    47. Metrics: inspections • Purpose: measure effectiveness of inspections • may reflect deficiencies of the development team, quality of code • Measure fault density • Faults per page - specs and design inspection • Faults per KLOC - code inspection • Fault detection rate - #faults / hour • Fault detection efficiency - #faults/person-hour CS 406 Testing

    48. Metrics: maintenance phase • Metrics related to the activities performed. What are they? • Specific metrics: • total number of faults reported • classifications by severity, fault type • status of fault reports (reported/fixed) CS 406 Testing

    49. References • Textbook • S. R. Scach - Classical and Object-Oriented Software Engineering (Look at metrics under Index) • Other books • P. Jalote - An Integrated Approach to Software Engineering (Look at metrics under Index) CS 406 Testing