CSE360 Introduction to Software Engineering Lecture 24: Software Measurement Javier Gonzalez-Sanchez [email protected] javiergs.engineering.asu.edu Office Hours: By appointment

Let’s Make a Parenthesis To talk about Quality

Javier Gonzalez-Sanchez | CSE360 | Summer 2017 | 3 Quality

Javier Gonzalez-Sanchez | CSE360 | Summer 2017 | 4 Software Metrics • Knowing what is inside your source code is the first step in assessing the quality of the software product. • Knowing the quantity of work performed in generating the source code is the first step in determining the productivity of your software team.

Javier Gonzalez-Sanchez | CSE360 | Summer 2017 | 5 Software Metrics Metrics Size LOC eLOC lLOC Understandability Comments Whitespaces Complexity Average, Max, Min LOC Interface Complexity Cyclomatic Complexity

Javier Gonzalez-Sanchez | CSE360 | Summer 2017 | 6 Size Metrics • LOC – Lines of Code Metric. Including lines of a single brace or parenthesis • LOC are used to create time and cost estimates. • LOC are a tracking tool to measure the degree of progress on a module or project. • An experienced developer can gage a LOC estimate based upon knowledge of past productivity on projects.

Javier Gonzalez-Sanchez | CSE360 | Summer 2017 | 7 Size Metrics • eLOC – effective Lines of Code Metric. Only code statements • An effective line of code or eLOC is the measurement of all lines that are not comments, blanks or standalone braces or parenthesis. These can inflate LOC metrics by 20 to 40 percent. • This metric more closely represents the quantity of work performed.

Javier Gonzalez-Sanchez | CSE360 | Summer 2017 | 8 Size Metrics • lLOC – logical Lines of Code Metric. • These statements are terminated with a semi- colon. The control line for the "for" loop contain two semi-colons but accounts for only one semi colon.

Javier Gonzalez-Sanchez | CSE360 | Summer 2017 | 9 Example

Javier Gonzalez-Sanchez | CSE360 | Summer 2017 | 10 Understandability Metrics • Comment Line and Comment Percent Metric • The degree of commenting within the source code measures the care taken by the programmer to make the source code and algorithms understandable. • Poorly commented code makes the maintenance phase of the software life cycle an extremely expensive adventure. • Comments can occur by themselves on a physical line or be co-mingled with source code. The sum of the lines of code, comments and blank lines often exceeds the physical line count. This is expected a when comments are co-mingled with source code. • Comment Percent = Comment Line Count / (LOC) x 100

Javier Gonzalez-Sanchez | CSE360 | Summer 2017 | 11 Understandability Metrics • Blank Line and White Space Percent Metric • The number of blank lines within source code determines the readability of the product. White space accents the logical grouping of constructs and variables. Programs which use few blank lines are difficult to read and more expensive to maintain. • It counts the spaces and characters within the source code. The white space percentage metric is another measure of readability for the source product. • White Space Percentage = (Number of spaces / Number of spaces and characters) * 100

Javier Gonzalez-Sanchez | CSE360 | Summer 2017 | 12 Reference Chapter 24

CSE360 – Introduction to Software Engineering Javier G onzalez-Sanchez javiergs@ asu.edu Sum m er 2017 Disclaim er. These slides can only be used as study m aterial for the class C SE360 at ASU. They cannot be distributed or used for another purpose.