Because of the naturalness of software and the
rapid evolution of Machine Learning (ML) techniques, frequently
repeated code change patterns (CPATs) occur often. They range
from simple API migrations to changes involving several complex
control structures such as for loops. While manually performing
CPATs is tedious, the current state-of-the-art techniques for
inferring transformation rules are not advanced enough to handle
unseen variants of complex CPATs, resulting in a low recall
rate. In this paper we present a novel, automated workflow
that mines CPATs, infers the transformation rules, and then
transplants them automatically to new target sites. We designed,
implemented, evaluated and released this in a tool, PYEVOLVE.
At its core is a novel data-flow, control-flow aware transformation
rule inference engine. Our technique allows us to advance the
state-of-the-art for transformation-by-example tools; without it,
70% of the code changes that PYEVOLVE transforms would not
be possible to automate. Our thorough empirical evaluation of
over 40,000 transformations shows 97% precision and 94% recall.
By accepting 90% of CPATs generated by PYEVOLVE in famous
open-source projects, developers confirmed its changes are useful.
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Retrieving Data Constraint Implementations Using Fine-Grained Code Patterns
Business rules are an important part of the requirements of software systems that are meant to support an organization. These rules describe the operations, definitions, and constraints that apply to the organization. Within the software system, business rules are often translated into constraints on the values that are required or allowed for data, called data constraints. Business rules are subject to frequent changes, which in turn require changes to the corresponding data constraints in the software. The ability to efficiently and precisely identify where data constraints are implemented in the source code is essential for performing such necessary changes.
In this paper, we introduce Lasso, the first technique that automatically retrieves the method and line of code where a given data constraint is enforced. Lasso is based on traceability link recovery approaches and leverages results from recent research that identified line-of-code level implementation patterns for data constraints. We implement three versions of Lasso that can retrieve data constraint implementations when they are implemented with any one of 13 frequently occurring patterns. We evaluate the three versions on a set of 299 data constraints from 15 real-world Java systems, and find that they improve method-level link recovery by 30%, 70%, and 163%, in terms of true positives within the first 10 results, compared to their text-retrieval-based baseline. More importantly, the Lasso variants correctly identify the line of code implementing the constraint inside the methods for 68% of the 299 constraints.
more » « less- Award ID(s):
- 1910976
- PAR ID:
- 10348231
- Publisher / Repository:
- Zenodo
- Date Published:
- Edition / Version:
- 1.1
- Subject(s) / Keyword(s):
- business rule code pattern data constraint traceability link recovery empirical study fine-grained traceability
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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