MAGPIE: Unified Software Improvement Framework

• MAGPIE is a collection of frameworks leveraging an edit-sequence abstraction to represent and apply minimal software changes for performance improvement.
• It employs a first-improvement local search strategy that iteratively mutates software variants and quantifies gains using metrics like speedup percentages.
• Experimental evaluations on systems like MiniSAT, LPG, and Weka demonstrate significant performance gains and highlight the framework's extensibility to various domains.

Magpie refers to a collection of frameworks and benchmark platforms across computational sciences, software engineering, machine learning, and physical simulation domains. Distinguished by the acronym “MAGPIE” in several notable systems and benchmarks, these frameworks share the trait of encoding, optimizing, or evaluating high-dimensional performance or behavioral properties using advanced algorithmic or learning-based instrumentation. The following entry articulates the core concepts and methodologies underpinning the primary frameworks named Magpie, focusing especially on the unified software improvement framework ("MAGPIE: Machine Automated General Performance Improvement via Evolution of Software" (Blot et al., 2022)), with references to ancillary applications employing the same or adapted terminology.

1. Common Edit-Sequence Representation in Automated Software Improvement

MAGPIE, as proposed by Blair, Petke, Langdon, and others (Blot et al., 2022), establishes a unified abstraction for representing software transformations: the edit sequence. Each software variant is encoded as an ordered sequence of atomic edits:

$E = \langle e_1, e_2, \ldots, e_k \rangle$

where each edit $$e_i = (\mathit{type}_i, \mathit{loc}_i, \mathit{ingr}_i)$$ comprises:

• $\mathit{type}_i$: {StmtDelete, StmtInsert, StmtReplace, ParamSet, ConstantSet, ConstantUpdate}
• $\mathit{loc}_i$: Precise location in AST/parameter space where the edit applies
• $\mathit{ingr}_i$: The new fragment, flag, or value to be assigned/inserted/replaced

Edits are minimalistic—one AST change or parameter tweak—enabling semantic transparency, traceable mutability, and ready translation to human-readable patches. This representation serves as the cornerstone for integrating heterogeneous improvement techniques under a common search interface.

2. Unified Local Search Engine and Workflow

MAGPIE operationalizes the edit-sequence abstraction by deploying a first-improvement local search strategy across the combined space of program changes. Initialization starts with $E_{\text{best}} = \langle\rangle$. Iterative search proceeds by mutating the current best edit sequence, generating a new software variant, measuring the optimization objective (e.g., runtime efficiency via fitness $f(P) = 1/\mathrm{runtime}(P)$), and accepting the mutation if it yields improvement.

Mutations encompass random edit insertions or deletions, sampled uniformly; after the search budget is exhausted, a validation step prunes non-contributing edits and reassembles the minimal effective subsequence. This decouples the “search process” from “what to change” and is applicable to compiler flags, algorithmic parameters, or genetic improvements—all encoded in the same abstraction.

3. Experimental Evaluation and Benchmarks

MAGPIE has been evaluated on four real-world software systems:

• MiniSAT (C++ SAT solver): 25 parameters, AST edits to Solver.cc
• LPG (C planner): 87 parameters, AST edits to H_relaxed.c
• Sat4J (Java SAT solver): 10 parameters, AST edits to Solver.java
• Weka (Java RandomForest classifier): 10 parameters, edits to RandomForest/RandomTree

Each program’s instances were partitioned using ten-fold cross validation, with metrics collected for median CPU instructions over multiple runs to mitigate non-deterministic wall-clock noise. Speedup $\Delta = 100\% \times \frac{T_0 - T_i}{T_0}$ quantifies relative performance gains over the original baseline.

4. Comparative Results and Synergy of Techniques

MAGPIE’s unified representation enables direct quantitative comparison and synthesis of methods such as compiler optimization, parameter tuning, and genetic improvement. Notable speedups for individual techniques include:

• Compiler optimization: 25%
• Algorithm configuration: 97%
• Genetic improvement (AST statements): 61%

When combining patches found by distinct methods, MAGPIE achieves further gains (e.g., MiniSAT_GCC: 25% → 35%), and joint search (simultaneous, unconstrained mixing of techniques) can reach up to 40% speedup. Synergistic effects arise from edits that span compilation flags, parameter maps, and source-level mutations—regions in the improvement space typically unreachable by isolated strategies.

5. Extensibility to Other Domains and Generalization

The MAGPIE representation is language-agnostic and flexible, facilitating adaptation to domains outside runtime performance. For example, in cache-miss reduction (MAGPIE for GI, (Langdon et al., 2023)), local search over line-level edits using Python—rather than AST—was conducted with fitness functions based solely on hardware L1 data cache misses, measured via perf_event, and verified for functional equivalence. In image processing kernels and spatial code (OLC, SEEDS), MAGPIE achieved up to 47% cache-miss reduction, with generalization verified on unseen test sets; however, gains in industrial kernels (H3) proved vulnerable to hardware noise and overfitting, motivating future profiling-driven constraints.

6. Analysis of Representation and Limitations

MAGPIE’s uniform edit-sequence encoding unlocks direct reuse of search engines and unbiased cross-technique comparison. All candidate changes—whether compiler, configuration, or program mutation—coexist, allowing arbitrary combinations. In practice, synergy manifests when edits from distinct domains interact to unlock improved performance unreachable by specialty domains alone. Limitations include reliance on basic local search, absence of high-level program transformations (e.g., data-structure refactoring), and small codebase scale. Benchmarks focused on a limited set of classical applications; broader adoption and further empirical validation are recommended.

7. Prospective Directions and Open Problems

Future work for MAGPIE encompasses:

• Deployment of advanced metaheuristic search (bandit/adaptive strategies)
• Enabling multi-objective optimization (e.g., energy, memory alongside speed)
• Construction of libraries for structural mutations (cache-blocking, tiling)
• Scaling to millions of lines of code via hierarchical edit-sequence modeling
• Integration with lightweight profiling to constrain edits to executed code regions
• Application to joint cache and runtime optimization, as well as extensions to databases and JIT kernels

Research indicates MAGPIE’s potential for rapid prototyping, comparative benchmarking, and extending automated improvement across software and hardware domains. Its codebase is open-source (https://github.com/bloa/magpie), facilitating further methodological innovation (Blot et al., 2022, Langdon et al., 2023).