Searcharxiv⌕ Search

arXiv subjects

W. B. Langdon

Publications and source records attributed to W. B. Langdon.

16 recordsLinked to original sources
arXiv2026

Hot Fixing in the Wild

Despite the operational importance of hot fixes, large-scale evidence on how they reshape routine maintenance workflows, particularly in the era of autonomous coding agents, remains limited. We analyse hot fixes present in over 61,000 GitHub repositories from the Hao-Li/AIDev dataset and find consistent patterns of urgency: reduced collaboration (typically a single contributor), smaller and more targeted changes (median 2-3 commits and files, with <10 line modifications), limited review (often fewer than two reviewers), and substantially fewer test file modifications than regular bug fixes, consistent with their urgency-driven character. Leveraging the same urgency contexts, we examine differences between human- and AI-agent-authored hot fixes, revealing over 10 distinct repair behaviours, thus offering insights into future human-automation collaboration for hot fixing. Our study is the first to empirically analyse hot fix code changes at scale using a repository-level operationalisation of urgency. The comparison of human and agentbehaviours delineates their distinct characteristics, providing a foundation for understanding hot fixing in real-world practice

cs.SE↗
arXiv2025

GreenMalloc: Allocator Optimisation for Industrial Workloads

We present GreenMalloc, a multi objective search-based framework for automatically configuring memory allocators. Our approach uses NSGA II and rand_malloc as a lightweight proxy benchmarking tool. We efficiently explore allocator parameters from execution traces and transfer the best configurations to gem5, a large system simulator, in a case study on two allocators: the GNU C/CPP compiler's glibc malloc and Google's TCMalloc. Across diverse workloads, our empirical results show up to 4.1 percantage reduction in average heap usage without loss of runtime efficiency; indeed, we get a 0.25 percantage reduction.

cs.SE↗
arXiv2025

GA4GC: Greener Agent for Greener Code via Multi-Objective Configuration Optimization

Coding agents powered by LLMs face critical sustainability and scalability challenges in industrial deployment, with single runs consuming over 100k tokens and incurring environmental costs that may exceed optimization benefits. This paper introduces GA4GC, the first framework to systematically optimize coding agent runtime (greener agent) and code performance (greener code) trade-offs by discovering Pareto-optimal agent hyperparameters and prompt templates. Evaluation on the SWE-Perf benchmark demonstrates up to 135x hypervolume improvement, reducing agent runtime by 37.7% while improving correctness. Our findings establish temperature as the most critical hyperparameter, and provide actionable strategies to balance agent sustainability with code optimization effectiveness in industrial deployment.

cs.SE↗
arXiv2022

A Trillion Genetic Programming Instructions per Second

We summarise how a 3.0 GHz 16 core AVX512 computer can interpret the equivalent of up to on average 1103370000000 GPop/s. Citations to existing publications are given. Implementation stress is placed on both parallel computing, bandwidth limits and avoiding repeated calculation. Information theory suggests in digital computing, failed disruption propagation gives huge speed ups as FDP and incremental evaluation can be used to reduce fitness evaluation time in phenotypically converged populations. Conversely FDP may be responsible for evolution stagnation. So the wider Evolutionary Computing, Artificial Life, Unconventional Computing and Software Engineering community may need to avoid deep nesting.

cs.NE↗
arXiv2021

Evolving Open Complexity

Information theoretic analysis of large evolved programs produced by running genetic programming for up to a million generations has shown even functions as smooth and well behaved as floating point addition and multiplication loose entropy and consequently are robust and fail to propagate disruption to their outputs. This means, while dependent upon fitness tests, many genetic changes deep within trees are silent. For evolution to proceed at reasonable rate it must be possible to measure the impact of most code changes, yet in large trees most crossover sites are distant from the root node. We suggest to evolve very large very complex programs, it will be necessary to adopt an open architecture where most mutation sites are within 10 to 100 levels of the organism's environment.

cs.NE↗
arXiv2019

Faster Genetic Programming GPquick via multicore and Advanced Vector Extensions

We evolve floating point Sextic polynomial populations of genetic programming binary trees for up to a million generations. Programs with almost four hundred million instructions are created by crossover. To support unbounded Long-Term Evolution Experiment LTEE GP we use both SIMD parallel AVX 512 bit instructions and 48 threads to yield performance of up to 139 billion GP operations per second, 139 giga GPops, on a single Intel Xeon Gold 6126 2.60GHz server.

cs.NE↗
arXiv2018

The Distribution of Reversible Functions is Normal

The distribution of reversible programs tends to a limit as their size increases. For problems with a Hamming distance fitness function the limiting distribution is binomial with an exponentially small chance (but non~zero) chance of perfect solution. Sufficiently good reversible circuits are more common. Expected RMS error is also calculated. Random unitary matrices may suggest possible extension to quantum computing. Using the genetic programming (GP) benchmark, the six multiplexor, circuits of Toffoli gates are shown to give a fitness landscape amenable to evolutionary search. Minimal CCNOT solutions to the six multiplexer are found but larger circuits are more evolvable.

cs.ET↗
arXiv2017

Long-Term Evolution of Genetic Programming Populations

We evolve binary mux-6 trees for up to 100000 generations evolving some programs with more than a hundred million nodes. Our unbounded Long-Term Evolution Experiment LTEE GP appears not to evolve building blocks but does suggests a limit to bloat. We do see periods of tens even hundreds of generations where the population is 100 percent functionally converged. The distribution of tree sizes is not as predicted by theory.

cs.NE↗
arXiv2015

Genetically Improved BarraCUDA

BarraCUDA is a C program which uses the BWA algorithm in parallel with nVidia CUDA to align short next generation DNA sequences against a reference genome. The genetically improved (GI) code is up to three times faster on short paired end reads from The 1000 Genomes Project and 60percent more accurate on a short BioPlanet.com GCAT alignment benchmark. GPGPU Barracuda running on a single K80 Tesla GPU can align short paired end nextgen sequences up to ten times faster than bwa on a 12 core CPU.

q-bio.GN↗
arXiv2013

Using Genetic Programming to Model Software

We study a generic program to investigate the scope for automatically customising it for a vital current task, which was not considered when it was first written. In detail, we show genetic programming (GP) can evolve models of aspects of BLAST's output when it is used to map Solexa Next-Gen DNA sequences to the human genome.

cs.NE↗
arXiv2013

Which is faster: Bowtie2GP > Bowtie > Bowtie2 > BWA

We have recently used genetic programming to automatically generate an improved version of Langmead's DNA read alignment tool Bowtie2 Sect.5.3 RN/12/09. We find it runs more than four times faster than the Bioinformatics sequencing tool (BWA) currently used with short next generation paired end DNA sequences by the Cancer Institute, takes less memory and yet finds similar matches in the human genome.

q-bio.GN↗
arXiv2011

More Mouldy Data: Another mycoplasma gene jumps the silicon barrier into the human genome

The human genome sequence database contains DNA sequences very like those of mycoplasma molds. It appears such moulds infect not only molecular Biology laboratories but were picked up by experimenters from contaminated samples and inserted into GenBank as if they were human. At least one mouldy EST (Expressed Sequence Tag) has transferred from public databases to commercial tools (Affymetrix HG-U133 plus 2.0 microarrays). We report a second example (DA466599) and suggest there is a need to clean up genomic databases but fear current tools will be inadequate to catch genes which have jumped the silicon barrier.

q-bio.GN↗
arXiv2010

Automated DNA Motif Discovery

Ensembl's human non-coding and protein coding genes are used to automatically find DNA pattern motifs. The Backus-Naur form (BNF) grammar for regular expressions (RE) is used by genetic programming to ensure the generated strings are legal. The evolved motif suggests the presence of Thymine followed by one or more Adenines etc. early in transcripts indicate a non-protein coding gene. Keywords: pseudogene, short and microRNAs, non-coding transcripts, systems biology, machine learning, Bioinformatics, motif, regular expression, strongly typed genetic programming, context-free grammar.

q-bio.BM↗
arXiv2010

RNAnet a Map of Human Gene Expression

RNAnet provides a bridge between two widely used Human gene databases. Ensembl describes DNA sequences and transcripts but not experimental gene expression. Whilst NCBI's GEO contains actual expression levels from Human samples. RNAnet provides immediate access to thousands of Affymetrix HG-U133 2plus GeneChip measurements Homo sapiens genes in most medically interesting tissues. Without RNAnet comparison across experiments in GEO is very labour intensive requiring man months of effort to down load and clean data. With RNAnet anyone can access cleaned quantile normalised data in seconds. It can be used to data mine patterns of co-expression. The network of strongly correlated genes is huge but sparse. Thousands of genes interact strongly with thousands of others. Conversely there are tens of thousands of genes which interact strongly with less than 100 others. I.e. RNAnet gives new views for RNA Systems Biology.

q-bio.MN↗