Suffix Array + Longest Common Prefix (LCP) for Code Clone Detection

Suffix Arrays and Longest Common Prefix (LCP) arrays, originally developed for string processing and bioinformatics, are now used in Large Language Model (LLM) data pipelines for exact substring-level code clone detection, identifying duplicated code blocks and repeated programming patterns that document-level algorithms like MinHash LSH miss. Unlike probabilistic methods, Suffix Arrays perform exact matching, guaranteeing every discovered duplicate exists in the corpus, which is critical for training data cleaning, boilerplate removal, and plagiarism analysis in large software repositories.

Suffix Arrays and Longest Common Prefix LCP arrays are among the most efficient algorithms for detecting repeated substrings within very large sequences. Originally developed for string processing, text indexing, and bioinformatics, these algorithms have become equally valuable in modern Large Language Model LLM data pipelines, particularly for identifying duplicated code blocks, boilerplate implementations, generated source code, and repeated programming patterns. Unlike document-level deduplication algorithms such as MinHash LSH, which estimate similarity between entire documents, Suffix Arrays operate at the substring level, allowing them to identify duplicated regions even when the surrounding files are completely different. This distinction is particularly important for source code. Large software repositories frequently contain copied utility functions, repeated license headers, generated serialization code, template implementations, macro expansions, and partially duplicated methods. These duplicated regions may occupy only a small fraction of the overall file, making document-level similarity insufficient for identifying them. Detecting these shared fragments requires an algorithm capable of finding long common token sequences across millions of code chunks. The Suffix Array provides exactly this capability. By sorting every suffix of a token sequence into lexicographic order, the algorithm places similar substrings adjacent to one another. The accompanying Longest Common Prefix array then measures how many consecutive tokens neighboring suffixes share. Long repeated code fragments therefore become immediately visible as large LCP values without requiring exhaustive pairwise comparison between every possible substring. Unlike probabilistic algorithms such as MinHash, Suffix Arrays perform exact substring matching. Every duplicated token sequence discovered by the algorithm is guaranteed to exist within the original corpus, making the method particularly valuable when exact clone detection is required for training data cleaning, boilerplate removal, plagiarism analysis, cache optimization, and repository indexing. Modern software development encourages code reuse. Developers frequently copy utility functions, adapt existing implementations, duplicate configuration templates, reuse generated code, and maintain similar implementations across multiple repositories. Consequently, large code corpora contain not only duplicated files but also thousands of partially duplicated code fragments embedded within otherwise unrelated projects. These repeated fragments introduce several challenges for language model training. Repeated implementations increase memorization, reduce corpus diversity, and bias optimization toward frequently occurring boilerplate rather than genuinely informative programming patterns. Furthermore, duplicated code inflates storage requirements and preprocessing costs while contributing relatively little additional knowledge to the model. A naïve solution would compare every substring within every file against every substring in every other file. Unfortunately, the number of possible substrings grows quadratically with document length, making exhaustive comparison computationally infeasible for datasets containing billions of tokens. Suffix Arrays solve this problem by transforming substring comparison into a sorting problem. Instead of comparing substrings individually, the algorithm sorts every suffix exactly once. Once sorted, similar substrings automatically become adjacent within the suffix array, allowing duplicate detection to proceed through a simple linear scan of the accompanying LCP array. This transformation reduces computational complexity dramatically while preserving exact matching accuracy. References Manber, U., & Myers, G.Suffix Arrays: A New Method for On-Line String Searches. Kasai, T., et al.Linear-Time Longest-Common-Prefix Computation in Suffix Arrays. Gusfield, D.Algorithms on Strings, Trees, and Sequences. FUNCTION construct suffix array via prefix doubling concatenated token sequence : total sequence length = length concatenated token sequence suffix rank array = BUILD ARRAY OF SIZE total sequence length suffix index array = BUILD ARRAY OF SIZE total sequence length FOR position index IN RANGE 0, total sequence length : suffix rank array position index = ordinal value concatenated token sequence position index suffix index array position index = position index comparison offset = 1 WHILE comparison offset < total sequence length: FUNCTION compare suffixes by current rank pair first position, second position : first primary rank = suffix rank array first position second primary rank = suffix rank array second position IF first primary rank = second primary rank: RETURN first primary rank - second primary rank first secondary rank = get rank or negative infinity suffix rank array, first position + comparison offset, total sequence length second secondary rank = get rank or negative infinity suffix rank array, second position + comparison offset, total sequence length RETURN first secondary rank - second secondary rank suffix index array = sort array using comparator suffix index array, compare suffixes by current rank pair updated rank array = BUILD ARRAY OF SIZE total sequence length updated rank array suffix index array 0 = 0 FOR array position IN RANGE 1, total sequence length : previous suffix position = suffix index array array position - 1 current suffix position = suffix index array array position ranks are identical = compare suffixes by current rank pair previous suffix position, current suffix position == 0 IF ranks are identical: updated rank array current suffix position = updated rank array previous suffix position ELSE: updated rank array current suffix position = updated rank array previous suffix position + 1 suffix rank array = updated rank array IF suffix rank array suffix index array total sequence length - 1 == total sequence length - 1: BREAK comparison offset = comparison offset 2 RETURN suffix index array The first stage of the algorithm transforms the complete token sequence into a Suffix Array. A suffix is simply the portion of the sequence beginning at a particular token position and extending to the end of the document. If a token sequence contains n positions, then exactly n suffixes exist. Rather than storing the suffixes themselves, the algorithm stores only their starting positions, significantly reducing memory consumption. The construction algorithm sorts these suffixes according to lexicographic order. As a result, suffixes beginning with similar token sequences naturally become neighbors within the sorted array. This property is fundamental because duplicated substrings always appear as common prefixes of neighboring suffixes after sorting. Modern implementations rarely perform explicit string sorting. Instead, specialized linear-time algorithms such as SA-IS or DC3 Difference Cover Modulo 3 exploit recursive partitioning and induced sorting to construct suffix arrays efficiently even for extremely large token sequences. These algorithms avoid repeated substring comparisons by organizing suffixes through progressively refined ordering relationships. The completed suffix array therefore functions as a searchable index over every possible suffix contained within the corpus. Subsequent stages of the algorithm rely entirely upon this sorted ordering rather than the original document organization. References Nong, G., Zhang, S., & Chan, W. H.Linear Suffix Array Construction by Almost Pure Induced Sorting. Kärkkäinen, J., & Sanders, P.Simple Linear Work Suffix Array Construction. FUNCTION construct lcp array via kasai algorithm concatenated token sequence, suffix index array : total sequence length = length concatenated token sequence inverse suffix index array = BUILD ARRAY OF SIZE total sequence length FOR array position IN RANGE 0, total sequence length : inverse suffix index array suffix index array array position = array position longest common prefix array = BUILD ARRAY OF SIZE total sequence length, fill value = 0 running common prefix length = 0 FOR original position IN RANGE 0, total sequence length : IF inverse suffix index array original position == total sequence length - 1: running common prefix length = 0 CONTINUE adjacent suffix position = suffix index array inverse suffix index array original position + 1 WHILE original position + running common prefix length < total sequence length AND adjacent suffix position + running common prefix length < total sequence length AND concatenated token sequence original position + running common prefix length == concatenated token sequence adjacent suffix position + running common prefix length : running common prefix length = running common prefix length + 1 longest common prefix array inverse suffix index array original position = running common prefix length IF running common prefix length 0: running common prefix length = running common prefix length - 1 RETURN longest common prefix array Although the suffix array identifies neighboring suffixes, it does not directly reveal how similar those suffixes actually are. This information is computed by the Longest Common Prefix array. For every neighboring pair of suffixes within the sorted suffix array, the LCP array records the number of consecutive tokens shared before the two suffixes diverge. The Kasai algorithm constructs the LCP array efficiently by exploiting relationships between adjacent suffixes. Instead of repeatedly comparing every suffix from the beginning, the algorithm reuses information computed during previous comparisons. Because neighboring suffixes frequently overlap substantially, the algorithm avoids redundant work by carrying forward the previously matched prefix length whenever possible. Internally, the algorithm first constructs an inverse permutation known as the rank array. This structure maps every suffix position back to its location within the sorted suffix array, allowing constant-time identification of neighboring suffixes. Using this mapping, the algorithm performs only incremental comparisons while progressively updating the current common prefix length. This optimization enables linear-time LCP construction despite the potentially enormous number of suffixes involved. The resulting LCP array transforms the suffix array from a sorted index into a powerful duplicate detection structure. Large LCP values immediately indicate long repeated token sequences shared between neighboring suffixes. References Kasai, T., et al.Linear-Time Longest-Common-Prefix Computation in Suffix Arrays. Gusfield, D.Algorithms on Strings, Trees, and Sequences. FUNCTION detect cross chunk clone regions concatenated token sequence, suffix index array, longest common prefix array, chunk boundary lookup table, minimum clone token length : total sequence length = length concatenated token sequence raw clone region list = EMPTY LIST array position = 1 WHILE array position < total sequence length: IF longest common prefix array array position < minimum clone token length: array position = array position + 1 CONTINUE matching group start index = array position - 1 matching group end index = array position WHILE matching group end index < total sequence length AND longest common prefix array matching group end index = minimum clone token length: matching group end index = matching group end index + 1 shared common prefix length for group = MINIMUM OVER RANGE longest common prefix array, matching group start index + 1, matching group end index suffix positions in group = suffix index array matching group start index : matching group end index + 1 FOR first group index IN RANGE 0, length suffix positions in group : FOR second group index IN RANGE first group index + 1, length suffix positions in group : first suffix start position = suffix positions in group first group index second suffix start position = suffix positions in group second group index first originating chunk = lookup chunk for position chunk boundary lookup table, first suffix start position second originating chunk = lookup chunk for position chunk boundary lookup table, second suffix start position IF first originating chunk == second originating chunk: CONTINUE raw clone region = BUILD RAW CLONE REGION first chunk identifier = first originating chunk, first start position = first suffix start position, second chunk identifier = second originating chunk, second start position = second suffix start position, shared token length = shared common prefix length for group APPEND raw clone region list, raw clone region array position = matching group end index RETURN raw clone region list Real-world code repositories consist of many independent files rather than one continuous token sequence. To enable clone detection across multiple chunks, the algorithm concatenates every chunk into a single token array while inserting unique sentinel separators between consecutive chunks. These sentinel tokens guarantee that suffixes cannot incorrectly extend across chunk boundaries, preserving the integrity of individual documents. Once the unified token sequence has been constructed, the suffix array and LCP array are generated exactly as they would be for a single document. The algorithm then scans the LCP array sequentially. Whenever neighboring suffixes share a common prefix whose length exceeds a predefined threshold, the corresponding token regions become candidate code clones. Each suffix position must then be mapped back to its originating chunk. Rather than storing explicit document identifiers for every token, the algorithm maintains a sorted list of chunk boundaries. A binary search over these boundaries efficiently determines which original chunk contains each suffix position, allowing duplicated regions to be associated with their source files. This mapping stage converts low-level token offsets into meaningful clone relationships between code chunks. Rather than merely reporting repeated substrings, the algorithm identifies precisely which files share duplicated implementations and where those implementations begin. References Manber, U., & Myers, G.Suffix Arrays: A New Method for On-Line String Searches. Gusfield, D.Algorithms on Strings, Trees, and Sequences. FUNCTION construct and resolve clone pairs raw clone region list, minimum clone token length : normalized clone region list = EMPTY LIST FOR EACH raw clone region IN raw clone region list: normalized region = normalize pair ordering raw clone region, ORDER BY = first chunk identifier, first start position APPEND normalized clone region list, normalized region normalized clone region list = sort by chunk pair then start position normalized clone region list merged clone pair list = EMPTY LIST current merged region = NULL FOR EACH normalized region IN normalized clone region list: IF current merged region == NULL: current merged region = normalized region CONTINUE SAME CHUNK PAIR = normalized region.first chunk identifier == current merged region.first chunk identifier AND normalized region.second chunk identifier == current merged region.second chunk identifier FIRST REGION OVERLAPS = normalized region.first start position <= current merged region.first start position + current merged region.shared token length SECOND REGION OVERLAPS = normalized region.second start position <= current merged region.second start position + current merged region.shared token length IF SAME CHUNK PAIR AND FIRST REGION OVERLAPS AND SECOND REGION OVERLAPS: merged first end = MAXIMUM current merged region.first start position + current merged region.shared token length, normalized region.first start position + normalized region.shared token length merged second end = MAXIMUM current merged region.second start position + current merged region.shared token length, normalized region.second start position + normalized region.shared token length current merged region.shared token length = merged first end - current merged region.first start position ELSE: APPEND merged clone pair list, current merged region current merged region = normalized region IF current merged region = NULL: APPEND merged clone pair list, current merged region final clone pair list = EMPTY LIST FOR EACH merged region IN merged clone pair list: IF merged region.shared token length < minimum clone token length: CONTINUE clone pair = BUILD CLONE PAIR OBJECT first chunk identifier = merged region.first chunk identifier, first token range = BUILD RANGE merged region.first start position, merged region.first start position + merged region.shared token length , second chunk identifier = merged region.second chunk identifier, second token range = BUILD RANGE merged region.second start position, merged region.second start position + merged region.shared token length , cloned token length = merged region.shared token length APPEND final clone pair list, clone pair RETURN final clone pair list Every sufficiently large LCP value represents a duplicated substring, but multiple neighboring suffixes frequently describe overlapping regions of the same clone. Reporting every individual match would therefore produce many redundant clone pairs describing essentially identical duplicated code. To eliminate this redundancy, the algorithm groups overlapping matches into unified clone regions. Adjacent duplicate intervals sharing substantial overlap are merged into a single clone representation describing the complete duplicated block rather than numerous fragmented matches. This consolidation significantly improves both storage efficiency and interpretability while preserving all meaningful clone information. The resulting clone pairs record the originating chunks, the starting offsets within each chunk, and the length of the duplicated token sequence. These records provide the foundation for downstream processing, including dataset deduplication, boilerplate removal, repository analysis, cache optimization, plagiarism detection, and retrieval acceleration. One important advantage of Suffix Array–based clone detection is that every reported duplicate corresponds to an exact shared token sequence. Unlike probabilistic similarity algorithms, no statistical approximation is involved. The detected clone is guaranteed to exist exactly as reported within both source locations. This deterministic behavior makes Suffix Arrays particularly valuable whenever precise clone localization is required. References Baker, B. S.On Finding Duplication and Near-Duplication in Large Software Systems. Kamiya, T., Kusumoto, S., & Inoue, K.CCFinder: A Multilinguistic Token-Based Code Clone Detection System. Gusfield, D.Algorithms on Strings, Trees, and Sequences. name: suffix array lcp code clone detection pipelineon: chunk collection ready trigger: description: Fires once a full set of code chunks e.g. from AST-based chunking is ready to be scanned for clonesenv: minimum clone token length: shortest shared token run that counts as a real clone, filters out trivial matches like single tokens or short syntax fragments unique chunk separator token: sentinel inserted between chunks in the concatenated sequence, guaranteed to not occur in any real token streamjobs: stage 1 concatenate chunks with separators: description: Join every chunk's token sequence into one long sequence, with unique separator tokens marking chunk boundaries depends on: none, entry point for chunk collection ready trigger inputs: chunk collection: list of chunks, each with a token sequence and chunk identifier steps: - id: build concatenated sequence and boundary table run: FOR EACH chunk IN chunk collection → append token sequence, append unique chunk separator token, record start/end positions loop type: fixed-count iteration over the chunk collection why separators are required and not optional: Without a separator, a suffix from the tail of one chunk could naturally continue into the head of the next chunk's tokens, since they are physically adjacent in the concatenated array. This would let the suffix array treat token runs that cross a chunk boundary as if they were a real match, when they are actually two unrelated chunks placed next to each other by construction, not by content similarity. why each separator must be unique per chunk and not a single shared symbol: If every chunk used the same separator symbol, a suffix starting exactly at a separator in chunk A would appear identical to a suffix starting at a separator in chunk B, since both begin with the same symbol. A single shared separator would therefore itself become a spurious "clone" detected between every pair of chunks. A unique separator per chunk guarantees no two separators ever match each other. outputs: concatenated token sequence: single token array representing the entire chunk collection chunk boundary lookup table: mapping from any position in the concatenated sequence back to its originating chunk identifier stage 2 suffix array construction: description: Build an array of all suffix starting positions, sorted lexicographically by suffix content depends on: stage 1 concatenate chunks with separators inputs: concatenated token sequence: from stage 1 steps: - id: initialize ranks from single tokens run: FOR position index IN RANGE 0, total sequence length → suffix rank array position index = ordinal value of that single token - id: prefix doubling loop run: WHILE comparison offset < total sequence length → sort suffixes by current rank, rank at offset , reassign compressed ranks, double comparison offset loop type: doubling loop, exponentially growing comparison window, not a fixed-count loop why prefix doubling and not naive full suffix comparison: Naively comparing full suffixes against each other to sort them would cost time proportional to sequence length for every single comparison, compounding into a very expensive total sort. Prefix doubling instead sorts suffixes by their first 1 token, then effectively by their first 2 tokens using the previous round's ranks as a shortcut , then first 4, then first 8, and so on, reusing all prior work at each step instead of re-scanning full suffixes from scratch every round. why the loop terminates when ranks become fully unique: Once every suffix has a distinct rank, all suffixes are already fully and correctly ordered relative to each other; continuing to double the comparison offset past this point would do identical, wasted work, since no further comparisons could change the already-unique ordering. effect on downstream if this stage is wrong: The entire rest of the pipeline treats adjacency in suffix index array as a proxy for content similarity. Any error in the sort here means truly similar suffixes may end up far apart in the array, making them invisible to stage 4's adjacent-only scanning regardless of how correct stage 3 and stage 4 are individually. outputs: suffix index array: array of starting positions, ordered so that adjacent entries correspond to lexicographically adjacent suffixes stage 3 lcp array construction: description: For each adjacent pair of suffixes in sorted order, compute how many leading tokens they share depends on: stage 2 suffix array construction inputs: concatenated token sequence: from stage 1 suffix index array: from stage 2 steps: - id: build inverse suffix index run: FOR array position IN RANGE 0, total sequence length → inverse suffix index array suffix index array array position = array position why the inverse index is needed: Kasai's algorithm processes suffixes in original document order for efficiency reasons explained below, but needs to know each suffix's position in the sorted suffix array to know where to write its LCP value. The inverse index provides that lookup in constant time instead of a linear search per suffix. - id: kasai linear scan with shrinking reuse run: FOR original position IN RANGE 0, total sequence length → extend running common prefix length from previous suffix's leftover match, decrement by at most one per step loop type: single linear scan with a bounded-decrement invariant, not a fresh comparison from zero each time why processing in original order and not sorted order: Kasai's key insight is that the suffix starting at position i+1 in the original sequence shares at least running common prefix length - 1 tokens with its own sorted-order neighbor, because removing the first token from two suffixes that matched for L tokens leaves suffixes that still match for at least L-1 tokens. This reuse is only available when moving through original positions in order; processing in sorted order would discard this relationship and force a full recomparison from scratch at every single position. why this gives linear time instead of quadratic: Because running common prefix length only ever decreases by at most one per step before the inner comparison loop resumes extending it, the total number of comparison operations across the entire scan is bounded by twice the sequence length, rather than by sequence length multiplied by average match length. outputs: longest common prefix array: array where entry at sorted position p gives the shared prefix length between the suffixes at sorted positions p-1 and p stage 4 cross chunk clone region detection: description: Scan the sorted suffixes for runs of high LCP values, then filter matches down to only those spanning two different chunks depends on: stage 3 lcp array construction, stage 1 concatenate chunks with separators inputs: suffix index array: from stage 2 longest common prefix array: from stage 3 chunk boundary lookup table: from stage 1 minimum clone token length: from env steps: - id: group consecutive high lcp entries run: WHILE array position < total sequence length → extend matching group end index while longest common prefix array stays at or above minimum clone token length loop type: sequential scan with a variable-width grouping condition why grouping and not comparing only immediate neighbors: A run of three or more suffixes can all share the same long common prefix with each other, not just with their immediate sorted neighbor. Only checking immediate pairs LCP array entries one at a time in isolation would miss matches between suffixes that are two or more positions apart in the sorted array but still share the full run's common prefix. why the threshold check uses minimum clone token length here too: This is the same threshold used later in stage 5's final filter, applied early as a pruning step. Grouping only regions that already meet the minimum length avoids wasting the expensive pairwise chunk-comparison work in the next step on short, uninteresting matches that would be discarded anyway. - id: pairwise chunk origin check within group run: FOR first group index → FOR second group index first group index → look up originating chunk for each suffix start position, keep pair only if chunks differ loop type: nested loop over all pairs within a matching group, not just adjacent entries why all pairs within the group and not just consecutive ones: Any two suffixes within the same high-LCP group share at least the group's common prefix length with each other, by construction. Only checking consecutive pairs would miss valid cross-chunk clone pairs where the two matching suffixes are not directly adjacent in the sorted array but are still both within the same group. why same chunk pairs are discarded here: A long repeated pattern within a single chunk e.g. a loop body duplicated by hand inside one function is a within-chunk repetition concern, not a cross-chunk clone. This pipeline is specifically scoped to detecting duplication between different chunks, so same-chunk matches are filtered out at the earliest point they can be identified, rather than being carried forward and filtered later. outputs: raw clone region list: unmerged, potentially overlapping list of cross-chunk matching regions, each with two positions and a shared token length stage 5 clone pair construction and overlap resolution: description: Normalize, sort, and merge overlapping raw clone regions into final non-redundant clone pairs depends on: stage 4 cross chunk clone region detection inputs: raw clone region list: from stage 4 minimum clone token length: from env steps: - id: normalize and sort run: normalize each region's ordering, sort by first chunk identifier, first start position why normalization is needed: Because stage 4's nested loop can discover the same underlying pair of chunks in either order depending on which suffix appeared first in the group, without normalizing to a consistent first chunk, second chunk ordering, the same real clone could appear twice in the list as two superficially different entries, confusing the merge step that follows. - id: sequential merge of overlapping regions run: FOR EACH normalized region → merge into current merged region if same chunk pair and both positions overlap, otherwise close out current merged region and start a new one loop type: sequential accumulation with a merge-or-flush condition, relies on the prior sort step for correctness why overlap merging is necessary at all: A single real clone spanning many tokens can be broken into several overlapping raw clone region entries by stage 4, because multiple different suffix starting positions within that same clone all independently satisfy the minimum clone token length grouping condition. Without merging, one real clone would be reported as many fragmented, overlapping "clones," inflating clone counts and confusing any downstream consumer trying to count or visualize actual duplication. why this depends on the sort from the previous step: The sequential merge only correctly catches overlaps between consecutive entries in the list. If entries were not sorted by chunk pair and position first, two overlapping regions could end up far apart in list order and never be compared against each other at all, leaving the fragmentation unresolved. - id: final length filter run: IF merged region.shared token length < minimum clone token length THEN discard why a second filter after merging and not relying on stage 4s filter alone: This check exists for symmetry and safety, not because merging can shrink a region below threshold in the current merge logic; it guards against any merged region construction path that could theoretically produce a shorter combined length than expected, ensuring the threshold contract is enforced at the pipeline's actual exit point, not only at an intermediate step. outputs: final clone pair list: clean, non-overlapping list of cross-chunk clone pairs, each with concrete token ranges in both chunks, ready for reporting or downstream refactoring tools critical dependency chain: stage 1 separator uniqueness: controls: whether any match found later is even a legitimate content match versus a boundary artifact if this breaks: stage 4 either misses real cross-chunk clones near boundaries over-broken sequence or reports every chunk as cloned with every other chunk shared separator symbol treated as matching content stage 2 suffix sort correctness: controls: whether stage 3's Kasai algorithm produces valid LCP values at all, since Kasai assumes a correctly sorted suffix index array as a precondition if this breaks: longest common prefix array becomes meaningless, and every downstream stage operates on incorrect data without any explicit error being raised stage 3 lcp correctness: controls: exactly which suffix groups stage 4 considers as candidate matches if this breaks: real clones produce LCP values that don't cross minimum clone token length and are silently skipped, or unrelated suffixes appear to match and get carried forward as false clones stage 4 same chunk filtering: controls: whether the pipeline's output is actually about cross-chunk duplication or gets polluted with within-chunk repetition if this breaks: the final clone report mixes two conceptually different phenomena, within-file loops or repeated patterns versus real duplication across separate chunks, making the output unusable for a cross-chunk-specific refactoring or deduplication decision stage 5 overlap merge correctness: controls: whether one real clone is reported once, or reported as many fragmented overlapping entries if this breaks: clone counts and total duplicated-token statistics become inflated and unreliable, even though the underlying detected positions in stage 4 were individually correct Suffix Arrays and Longest Common Prefix LCP arrays offer a deterministic and computationally efficient solution for exact code clone detection in large-scale software repositories. Unlike document-level similarity methods, they identify duplicated code at the substring level, enabling precise localization of repeated implementations even when they appear within otherwise unrelated files. The algorithm transforms the expensive problem of exhaustive substring comparison into an efficient sequence of suffix sorting, LCP computation, and linear scanning. This enables scalable detection of duplicated code fragments while preserving exact matching guarantees. By mapping detected duplicates back to their original source files and consolidating overlapping matches, the approach produces meaningful clone groups suitable for downstream analysis. As modern software repositories continue to expand and AI systems increasingly rely on high-quality training corpora, exact clone detection remains an essential preprocessing step. Suffix Arrays combined with LCP provide a robust foundation for repository deduplication, boilerplate removal, plagiarism detection, retrieval optimization, and improving the diversity and quality of datasets used by large language models. Their combination of theoretical efficiency, deterministic accuracy, and scalability has established them as one of the fundamental algorithms for large-scale code analysis and preprocessing. Suffix Array + Longest Common Prefix LCP for Code Clone Detection https://blog.stackademic.com/suffix-array-longest-common-prefix-lcp-for-code-clone-detection-0513301723f3 was originally published in Stackademic https://blog.stackademic.com on Medium, where people are continuing the conversation by highlighting and responding to this story.