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Applied Software Measurement: shame about the made up numbers

“Applied Software Measurement” by Capers Jones (no relation) is widely cited, but I find it a very frustrating book; while the text is full of useful commentary the numbers in the tables are mostly made up or estimated from other numbers (some of which may be actual measurements).

This book’s many tables of numbers will catch the attention of anyone searching for software engineering data (which until recently was very hard to find). However, closer inspection of the numbers suggests that the real purpose of the empirical looking tables is to serve as eye-candy to impress casual readers.

Let’s take as an example the analysis of multiple implementations, in different languages, of software implementing telephone switching system functionality. The numbers are from the third edition of the book (the one I have; code+data, separate paper discussing the numbers).

Below are the numbers from Table 2-7: Function Point and Source Code Sizes for 10 Versions
of the Same Project.

All those 1,500’s are a bit suspicious; the second paragraph under the table says “… the original sizes ranged from about 1,300 to 1,750 …”. Why has a gratuitous 15% error been introduced?

Where did the values for language level come from? I once wrote the semantics phase of a CHILL compiler in Pascal and would have put CHILL’s language level on a par with Ada83. Objective C at 11 is surely a joke. Language level is a number, so obviously we can take its average.

This table looks like it has one column of actual data. Oh, five pages before the table appears we are told that the Ada95 and Objective C results were modeled, i.e., made up. So there are really only eight real implementations, not ten.

Language     Function pts   Language level    LOC/Func. Pt.    Size in LOC
Assembly     1,500          1                 250              375,000
C            1,500          3                 127              190,500
CHILL        1,500          3                 105              157,500
PASCAL       1,500          4                  91              136,500
PL/I         1,500          4                  80              120,000
Ada83        1,500          5                  71              106,500
C++          1,500          6                  55               82,500
Ada95        1,500          7                  49               73,500
Objective C  1,500         11                  29               43,500
Smalltalk    1,500         15                  21               31,500
Average      1,500          6                  88              131,700

Moving on to Table 2-6, see below: “Staff Months of Effort for 10 Versions of the Same Software Project”

The use of two decimal places of accuracy is an obvious red flag for wanting to impress via appearance of accuracy.

Is the requirements effort based on one known value, or is it estimated? Why the jump in design time when we get to C++? Does management time really have such a strong dependency on language used?

Language    Require.. Design   Code     Test    Document Management  TOTAL Effort
Assembly    13.64     60.00    300.00   277.78  40.54     89.95      781.91
C           13.64     60.00    152.40   141.11  40.54     53.00      460.69
CHILL       13.64     60.00    116.67   116.67  40.54     45.18      392.69
PASCAL      13.64     60.00    101.11   101.11  40.54     41.13      357.53
PL/I        13.64     60.00     88.89    88.89  40.54     37.95      329.91
Ada83       13.64     60.00     76.07    78.89  40.54     34.99      304.13
C++         13.64     68.18     66.00    71.74  40.54     33.81      293.91
Ada95       13.64     68.18     52.50    63.91  40.54     31.04      269.81
Objective C 13.64     68.18     31.07    37.83  40.54     24.86      216.12
Smalltalk   13.64     68.18     22.50    27.39  40.54     22.39      194.64

The above table makes more sense when LOC is plotted against Total Effort (in man months), see below:

Effort against LOC, from Capers Jones.

The blue line is for the object oriented languages (i.e., the last four rows of the table) and the red line everything else. Those two straight lines fit the data so well; I think that Total Effort was calculated from LOC and various rules of thumb used to create percentages for requirements, design, code, test, documentation and management.

There is no new data in the above table, it was all calculated from LOC and informed arm waving.

Table 2-9 and Table 2-11 list Total Effort (which has been estimated from LOC) and columns of values created by dividing or multiplying this by other estimated values.

Table 2-12, see below: “Defect Potentials for 10 Versions of the Same Software Project”

What is a Defect Potentials?

Again, a sudden jump in the design numbers at C++.

Language    Require.. Design   Code      Document Bad Fix  TOTAL Defects
Assembly    1,500     1,875    7,500     900      1,060    12,835
C           1,500     1,875    3,810     900        728     8,813
CHILL       1,500     1,875    3,150     900        668     8,093
PASCAL      1,500     1,875    2,730     900        630     7,635
PL/I        1,500     1,875    2,400     900        601     7,276
Ada83       1,500     1,875    2,130     900        576     6,981
C++         1,500     2,025    1,650     900        547     6,622
Ada95       1,500     2,025    1,470     900        531     6,426
Objective C 1,500     2,025      870     900        477     5,772
Smalltalk   1,500     2,025      630     900        455     5,510

Plotting total defects against LOC (below) suggests that “Defect Potentials” is a number calculated from LOC, i.e., no connection with actual defects found.

Defects against LOC, from Capers Jones.

Table 2-13 lists Total Defects (which has been estimated from LOC) and columns of values created by dividing or multiplying this by other estimated values.

So the book contains six impressive looking tables whose numbers have been calculated in one way or another from lines of code. A very good description of the many other tables in the book.

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