Test-Driven Reverse Engineering (TDRE)
Test-Driven Reverse Engineering (TDRE)
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Another case study on TDRE. Provided: 2,938 lines of Python code which process a handful of large files to create a number of outputs. [Details can't be disclosed.] Objective: Refactor to distinguish between the overall sequence of transformational steps and the details of each individual step.
The code is almost purely procedural. There are 11 class definitions. 6 of these wrap built-in types with type conversion and null-handling. 1 is a new exception. 1 is a generic "table" that essentially duplicates features of SQLite. The remaining 3 are actually part of the problem domain.
One reason for reverse engineering is that the code has reached an intellectual limit. It's small, but "dense" with highly-optimized processing steps. The cohesion type is almost all "Temporal". Processing is grouped into successive processing loops; each loop contains a cluster of processing steps. Consequently, it's quite hard to tease apart the algorithm to get a "big picture" of what's going on. It's just a dense stand of trees. No forest.
Another reason for reverse engineering is to support the endless adaptation and modification of the code base. The program is a kind of "spreadsheet on steroids". This isn't a simplistic collection of cells and formulæ that permits simple what-if analysis. This is a more complex set of formulæ that would be challenging (but not impossible) to implement as a spreadsheet. The use case, however, is the spreasheet use case: think, tweak, create results, repeat.
Start with an Initial Survey of the legacy code base and sample files.
Create an Outline or "sketch" of the domain model and main program. This will be a modules (or a package) with comments and some preliminary class definitions. Little more.
Pick a processing Step in the legacy code. This often requires creating processing summaries of the legacy code. Most legacy code is procedural, so the processing tends to be sequential in nature.
Instrument the Legacy Code with print statements to gather data. This can be simple. The output can be challenging to interpret.
with open("tdre_results_1","w") as tdre:
# some legacy processing
print( "Case:", foo, bar, ", Expect:", baz, file=tdre )
From the output, Build Unit Test Cases. Fill in parts of the processing sequence and domain model. Debug code until the tests pass.
The Initial Survey locates several things.
- The usable, working modules. It appears that all reverse engineering involves a code base with dead or unused code. Even a small project (3,000 lines) will have a remarkable amount of dead code.
- Priorities for the implemented functionality. Not every "main" module is relevant.
- Example inputs and outputs.
If the software cannot be run (as is the case with organically developed systems that depend on large, complex corporate databases), then the example inputs and outputs may not actually match the software. If the software can be run, it should be run and the actuals compared against the samples to confirm that the code base supplied really produced the sample outputs.
Expect that the provided legacy code is slightly different from the code in production use. In some cases, this cannot be resolved; for example, when the executables are older than the source. In other cases, the code matches and no further work is required to establish the legacy baseline.
The sample outputs point in the direction of an acceptance test case. The sample output cannot be taken literally as the one-and-only acceptance test. While it's desirable for reverse engineering to reproduce the sample output, most reverse engineering will involve enhancements or bug fixes. Expect that errors will be found (or may be known to exist) in the sample output.
The outline is -- initially -- just generic MVP. There must be a domain model, some "presenter" that has the application logic, and some "view" for displaying the outputs.
In our case study, above, the "view" is a collection of (mostly text) output files. The model was undefined in the legacy code, which was all "presenter" application logic.
The goal was to extract the underlying model, break the application "presenter" logic into two layers (forest and trees) and build some views for each of the output files.
Pick a Processing Step
This can be challenging, depending on the legacy code base. There are two paths through a procedural code base.
- Back to Front. Start with the final results and unit test the final steps based on previous steps that will be defined later.
- Front to Back. Start with the first recognizable intermediate result based on the input files. Unit test the initial steps.
It's more rewarding to work front-to-back because progress can be shown a little more clearly.
A better architecture can be created by working back-to-front since dependencies are easier to understand.
Unit Test Volume, Edges and Corners
There are two unit test design challenges when doing reverse engineering.
- Volume. The sample data can be large. 100,000 rows of sample data is too many to test. Finding a "representative" subset is difficult. Generally, arbitrary subsets have to be used to get started. Once the application mostly works, more refined unit tests need to be created.
- Edge and Corner Cases. While the code may be riddled with if-statements, it can still be difficult to locate sample inputs that exercise the various conditions in the code. It's risky to create data -- we have to assume that the legacy code does unexpected things. In many cases, print statements have to be put into complex if statements to locate any actual data that exercises that logic path.
Once the unit tests are built, this is just Test-Driven Development (TDD).
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