About the Project

“CorSys” is a demonstrative program synthesizer, which synthesizes best-effort Python expressions while weighting the chance for mistakes in given user outputs, using various metrics for mistake probability evaluation. CorSys enumerates all possible expressions using the given syntax, limited to a specified syntax-tree height, using a bottom-up enumeration methodology and using Observational Equivalence for pruning equivalent expressions under the given set of input-output examples. For each expression whose outputs are not observationally equivalent to a previously seen expression, the specified metric grades the distance between the actual outputs and the expected ones. Finally, the synthesizer is able to return the best expression, under a criterion selected by the user. CorSys is using the Syntax-Guided Synthesis (SyGuS) methodology, and is given small-step specifications to work with Programming by Examples (PBE).

This project is based on a previous work by Peleg and Polikarpova (2020). This paper describes a technique for dealing efficiently with incorrect input-output specifications given by the user. In the paper, it is suggested to use a distance metric for rewarding more-likely-to-be-correct programs, specifically using Levenshtein distance. In this work, we generalize this concept of distance metric for various kinds of user mistakes, focusing on arithmetical mistakes (for example, rounding values and off-by-one calculation mistakes) and typing mistakes (for example, replacing similar-sounding letters and deleting a letter). Please read the paper for more details about this technique:

Peleg, Hila, and Polikarpova, Nadia. 2020. “Perfect Is the Enemy of Good: Best-Effort Program Synthesis.” In 34th European Conference on Object-Oriented Programming (ECOOP 2020), edited by Robert Hirschfeld and Tobias Pape, 166:2:1–30. Leibniz International Proceedings in Informatics (LIPIcs). Dagstuhl, Germany: Schloss Dagstuhl–Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.ECOOP.2020.2.

The work is submitted as the final project in the course “Software Synthesis and Automated Reasoning” (236347), at Taub Faculty of Computer Science, Technion - Israel Institute of Technology. The project was written by Orel Adivi (orel.adivi [at] cs.technion.ac.il) and Daniel Noor (daniel.noor [at] cs.technion.ac.il), and under the supervision of Matan Peled and assistant professor Shachar Itzhaky. The work was done in about a month, from 19 September 2022 to 18 October 2022. The project is released under MIT license.

Usage

The synthesizer main file is Synthesizer.py, which uses code implemented in src directory. For running the synthesizer, an installation of CPython 3.9 or CPython 3.10 is required (the implementation is platform independent, and was tested on Windows, macOS, and Linux).

The project uses NumPy, SciPy, and overrides Python libraries, which can be installed using Pip package installer:

python -m pip install -r requirements.txt

Then, running the synthesizer with --help flag gives the list of the parameters to provide:

python Synthesizer.py --help

The following output is given:

usage: Synthesizer.py [-h] -io INPUT_OUTPUT_FILE -s SEARCH_SPACE_FILE
                      [-m {DefaultMetric,NormalMetric,CalculationMetric,VectorMetric,HammingMetric,LevenshteinMetric,PermutationMetric,KeyboardMetric,HomophoneMetric}]
                      [-mp METRIC_PARAMETER]
                      [-t {match,accuracy,height,top,best_by_height,penalized_height,interrupt}]
                      [-tp TACTIC_PARAMETER] [-mh MAX_HEIGHT] [--statistics]

CorSys - Synthesizing best-effort python expressions while weighting the
chance for mistakes in given user outputs.

options:
  -h, --help            show this help message and exit
  -io INPUT_OUTPUT_FILE, --input-output INPUT_OUTPUT_FILE
                        the root for the input-output file
  -s SEARCH_SPACE_FILE, --search-space SEARCH_SPACE_FILE
                        the root for the search space file
  -m {DefaultMetric,NormalMetric,CalculationMetric,VectorMetric,HammingMetric,LevenshteinMetric,PermutationMetric,KeyboardMetric,HomophoneMetric}, --metric {DefaultMetric,NormalMetric,CalculationMetric,VectorMetric,HammingMetric,LevenshteinMetric,PermutationMetric,KeyboardMetric,HomophoneMetric}
                        the metric for the synthesizer (default =
                        'DefaultMetric')
  -mp METRIC_PARAMETER, --metric-parameter METRIC_PARAMETER
                        the parameter for the metric
  -t {match,accuracy,height,top,best_by_height,penalized_height,interrupt}, --tactic {match,accuracy,height,top,best_by_height,penalized_height,interrupt}
                        the tactic for the synthesizer (default = 'height')
  -tp TACTIC_PARAMETER, --tactic-parameter TACTIC_PARAMETER
                        the parameter for the tactic
  -mh MAX_HEIGHT, --max-height MAX_HEIGHT
                        the max height for the synthesizer to search (default
                        = 2)
  --statistics          whether to present statistics

For help with the synthesizer please read SUPPORT.md .

The --help flag (or -h) shows this message with the list of the parameters, the --max-height parameter (or -mh) sets the maximal syntax-tree height to generate expressions, and --statistics flag shows statistics about the synthesizer. The other flags are covered in the following sections.

Inputs and Outputs

The input-and-output pair examples are a major part of the specifications and have to be supplied in a Comma-separated values (CSV) file. The path to this file has to be provided in the --input-output parameter (or -io). The first row of the file must include the name of each variable, and in the last column, the symbolic name OUTPUT must appear to indicate the expected value (possibly with mistakes). After the first row, each row represents a single input-output example, where the value of each variable matches its name in the first row. This is a minimal example of this format:

x,y,OUTPUT
1,2,3
3,4,7
1,5,6
0,0,0
-1,-5,-6

Examples for input-and-output pair example files are available in the utils/examples directory.

Search Space (Grammar)

The synthesis process traverses a specified search space, given in a text (TXT) file. The path to this file has to be provided in the --search-space parameter (or -s). In this file, each line must start with EXP ::= (due to Python’s type system, we decided to treat all Python types orthogonally and we do not support different Grammar variables), and after it the expression template. The allowed variables for the expression templates are EXP1, EXP2, EXP3, EXP4, EXP5, EXP6, EXP7, EXP8, and EXP9, such as the number of the maximal Grammar variable matches the arity of the expression template.

EXP ::= 0
EXP ::= 1
EXP ::= x
EXP ::= [EXP1]
EXP ::= EXP1 + EXP2
EXP ::= len(EXP1)

Examples for search space files are available in the utils/examples directory. We have a built-in implementation for the following expression templates:

• Terminals - literals and variables.
• Unary operations - +, -, not, and ~.
• Binary operations - +, -, *, /, //, %, **, <<, >>, |, ^, &, and @.
• Boolean operations - and (of arity up to 5) and or (of arity up to 5).
• Subscripting - [ ] (of arity up to 5), subscripting (l[]), and slicing (l[::]).
• Functions - len, index, sorted, list(reversed()), count, join, capitalize, casefold, lower, title, upper, and abs.

In other cases, the value of the expression will be evaluated using Python’s eval. Please see BuiltinGrammar.txt for the list of the built-in expression templates.

Metrics

The distance between the actual outputs and the expected outputs is calculated by the selected metric. All the metrics share a similar interface, where each metric implements a distance function for each of the basic Python types - int, float, str, and list. Metrics are required to return 0.0 if the actual outputs and the expected outputs are totally equivalent, 1.0 if the actual outputs and the expected outputs are totally different, and any value between 0.0 and 1.0 in any other case. The metric has to be provided in the --metric parameter (or -m). For several metrics, an additional parameter, --metric-parameter (or -mp), is also required. The following values are available for the --metric parameter:

• DefaultMetric - this metric uses the default implementation for equality of values.
• NormalMetric - this metric uses a normal distribution function to determine the relative distance between two numbers. The --metric-parameter defines the standard deviation value for use.
• CalculationMetric - this metric considers two values closer if the differences between them can be explained by manual calculation mistakes.
• VectorMetric - this metric lets the user choose a vector distance function and then uses it to measure the distance between values. The --metric-parameter defines the vector distance function and can be one of braycurtis, canberra, correlation, cosine, jensenshannon, hamming, jaccard, russellrao, and yule.
• HammingMetric - this metric computes the Hamming distance between strings and normalizes it according to the string length.
• LevenshteinMetric - this metric computes the Levenshtein distance between strings and normalizes it according to the string length. The --metric-parameter defines whether to use the recursive implementation (with memoization), in the case the truth value is True, or the dynamic programming implementation, in the case the truth value is False.
• PermutationMetric - this metric considered lists equal if they contain the same elements, regardless of order.
• KeyboardMetric - this metric computes the distance between two characters based on the physical distance between their keys on a QWERTY keyboard.
• HomophoneMetric - this metric considers two strings closer if they are pronounced similarly.

Tactics

The criterion of which expression to return is defined by the --tactic parameter (or -t). For several tactics, an additional parameter, --tactic-parameter (or -tp), is also required. The following values are available for the --tactic parameter:

• match - the first expression whose distance value is equal or less than the defined value is returned. The --tactic-parameter defines the threshold distance for returning an expression and should be between 0.0 to the number of examples.
• accuracy - the first expression whose distance value, divided by the number of examples, is equal or less than the defined value is returned. The --tactic-parameter defines the threshold distance, after normalization, for returning an expression, and should be between 0.0 to 1.0.
• height - the best expression, among all possible expressions whose syntax-tree height is up to the defined value, is returned. Please note that the height threshold is defined by --max-height parameter, and --tactic-parameter is ignored.
• top - the best expressions, among all possible expressions whose syntax-tree height is up to the defined value, are returned, one in each line (in descending accuracy). The --tactic-parameter defines the number of expressions to return.
• best_by_height - the best expressions, among all possible expressions whose syntax-tree height is up to the defined value, are returned, one in each line, so each line represents a different syntax-tree height limit. Please note that the maximal syntax-tree height is defined by --max-height parameter, and --tactic-parameter is ignored.
• penalized_height - the best expression, among all possible expressions whose syntax-tree height is up to the defined, is returned. Each expression is penalized according to its syntax-tree height, so smaller expressions are preferred. The --tactic-parameter defines the penalty for each addition of one for the syntax-tree height and should be between 0.0 to 1.0.
• interrupt - the best expression, until finishing searching all possible expressions whose syntax-tree height is up to the defined or until keyboard interrupt (ctrl + c), is returned. The --tactic-parameter is ignored.

Benchmarks

In order to evaluate the performance of the synthesizer, we wrote a set of ten benchmarks, each having a single Grammar file and five input-output pair files (a total of 50 tests). Each of the benchmarks was built to demonstrate a different ability of the synthesizer, focusing on its unique abilities to correct incorrect input-output specifications. In order to run the synthesizer with all the benchmarks, the following script can be executed:

python RunAllBenchmarks.py

It is also possible to run specific benchmarks by mentioning them as command-line arguments. The script runs the synthesizer with each of the input-output pair files, with the relevant Grammar, and ensures the correctness of the output and the lack of other errors. The time that is required for each test is also printed. We ran the script and the output we got is available in results.txt.

The following benchmarks are available:

• benchmark_1 - this is a sanity benchmark, testing integer expression synthesis with DefaultMetric.
• benchmark_2 - this benchmark tests float expression synthesis with DefaultMetric.
• benchmark_3 - this benchmark tests string-related expression synthesis with DefaultMetric.
• benchmark_4 - this benchmark tests list-related expression synthesis with DefaultMetric.
• benchmark_5 - this is a numerical error benchmark, testing float expression synthesis with NormalMetric.
• benchmark_6 - this is a calculation error benchmark, testing integer expression synthesis with CalculationMetric.
• benchmark_7 - this is a typo benchmark, testing string expression synthesis with LevenshteinMetric.
• benchmark_8 - this is a typo benchmark, testing string expression synthesis with KeyboardMetric.
• benchmark_9 - this is a typo benchmark, testing string expression synthesis with HomophoneMetric.
• benchmark_10 - this is a list-element typo benchmark, testing list expression synthesis with HammingMetric.

Project Engineering

Design and Development

The project was designed in accordance with the object-oriented programming (OOP) principles. For security purposes, later commits were signed cryptographically, security Github Actions were enabled, and a SECURITY.md file was written. For documentation, a website is available and a SUPPORT.md file was written. The project was written using PyCharm Professional and was managed using GitHub.

Continuous Integration

In order to ensure the correctness of commits sent to the GitHub server, a continuous integration pipeline was set. These checks are run automatically for each pull request and each push. The following actions were set:

• Build - basic tests are run with the updated code, to ensure the lack of syntax errors.
• Benchmarks - all the benchmarks are run with the updated code, to ensure its correctness.
• Style check - the coding style is automatically checked using Flake8, to match the PEP8 coding standard.
• Vulnerabilities check - the updated code is checked to ensure it does not contain any known vulnerability.
• Dependency review - the dependencies are reviewed to check for any security issues.
• Website - the CorSys website is updated with the current information.
• Dependabot - the dependency versions (in requirements.txt) are updated regularly.

For the relevant actions, the checks were run in all the supported Python version (CPython 3.9 and CPython 3.10), and on all supported operating systems - Windows (Windows Server 2022), macOS (macOS Big Sur 11), and Linux (Ubuntu 20.04).

Suggestions for Future Research

During the month of work, we were able to develop CorSys and demonstrate its abilities. We suggest the following directions for future research:

• Adding additional metrics - there are currently nine supported metrics, which cover different kinds of possible user mistakes. Covering more kinds of mistakes is possible by implementing more metrics (for example, a metric that deals with typing with a constant offset of typing on a regular keyboard, which might be common with small keyboards). Additionally, a combination of existing metrics may be combined into a single metric, with uses different metrics for different types. As a proof of concept, we have implemented CombinedMetric.py and found the current design to work with generating a metric that combines existing ones. It is also possible to be generalized to a weighted metric, where the metric for each type is calculated using several existing ones.
• Analyzing the frequency of user mistakes - the metrics we generated are based on the mistakes we experienced as Python programmers. It might be helpful to analyze the frequency of general-purpose Python programmers for creating more relevant metrics.
• Dealing with incorrect input specifications - the current implementation assumes that the incorrect specifications are only in the output, assuming that mistaken input specifications are ‘linearly’ expressed as mistaken outputs. It is might be possible to find ways for dealing with mistaken input specifications independently of the output.
• Improving the efficiency with different implementations - we found that for several input types and for several metrics, the time that was required to traverse all the expressions with a syntax-tree height of up to two - was up to five minutes. In order to be used in real conditions, this speed has to be improved. It is possible to do so by implementing more efficient algorithms for the metrics, improving the implementation of the synthesis process (for example, not treating all the expression types orthogonally, as it is now), or implementing the synthesizer in a different, preferably compiled, programming language. The efficiency of the synthesizer can be tested by creating an interactive game, where the synthesizer is required to find a matching example for a given Grammar and a set of input-output pairs, faster than a human.
• Improving the efficiency with jitting - the performance of the implementation can be also improved using just-in-time (JIT) compilation. This can be achieved by using PyPy Python interpreter, which is not currently supported.
• Testing the current implementation - the correctness of the implementation is currently mainly checked by the benchmarks. Testing each Python file separately using unittests might help find hiding bugs. We have created a basic unittest testing framework for the project, and we tested a previous version of the file ExpressionGenerator.py using random numbers.
• Using the current implementation for different tasks - the current implementation is a Syntax-Guided Synthesis (SyGuS) synthesizer that is given small-step specifications to work with Programming by Examples (PBE). However, the implementation can be generalized for different methodologies of software synthesis, such as CounterExample-Guided Inductive Synthesis (CEGIS). For instance, a program minimizer can be built, so it suggests to the user a smaller expression whose values are close enough.

Please feel free to contact us with any questions you have about CorSys.