Beyond Surface Metrics: Multi-Condition Requirements in Enterprise Maintenance and AI Coding Benchmarks

Heeyong Eun

cs.SENov 2, 2026

Beyond Surface Metrics: Multi-Condition Requirements in Enterprise Maintenance and AI Coding Benchmarks

Authors: Heeyong Eun

Organizations: KPMG Korea

Abstract

Repository-level AI coding benchmarks measure task success but do not directly capture the conditions required for software maintenance completion. We study multi-condition fulfillment, where a maintenance task must satisfy multiple operational conditions, sometimes with directional dependencies. Using a fixed 18-criterion taxonomy, we analyze 2,925 Apache Fineract issues and 944 tasks from SWE-bench Pro, SWE-PolyBench Java, and SWE-EVO. We examine condition breadth, corpus-level co-occurrence, and source-grounded directional requirement relationships. Among 1,177 benchmark task pairs matched on issue classification, modified-file count, and code churn, the mean fulfillment-set Jaccard distance is 0.8138, and 57.4% have disjoint condition sets. Condition count alone does not explain this contrast: SWE-bench Pro averages 2.47 conditions per task, versus 1.78 for Fineract. Temporal (B4) and numerical (B2) conditions have a phi coefficient of +0.430 in Fineract versus -0.013 in the pooled benchmark corpus. Among temporally tagged tasks, numerical conditions are also specified in 48.85% (254/520) of Fineract cases versus 5.81% (5/86) of benchmark cases. We document 16 directional relationships in Fineract requirements involving time, calculation, and state preservation, and inspect four benchmark cases to illustrate how relevant conditions appear in evaluation assertions. These examples do not quantify benchmark-wide oracle omissions. The results show that similar surface task profiles can mask substantially different fulfillment-condition sets. We recommend reporting condition breadth, co-occurrence, and directional relationships separately. The corpus comparisons are descriptive and may reflect domain differences; they do not establish causality or production failure rates.

Figures & tables

Analytical Dimension	SNC Audit [ 5 ]	UTBoost [ 7 ]	This Work
Primary Problem	Repository topography ambiguity and task demand categorization	Incomplete tests and false pass judgments	Multi-condition structure of maintenance completion
Unit of Analysis	Repository graph metrics (AST distance, spread, novelty)	Tests and candidate patches in SWE-bench tasks	Requirement fulfillment invariants ( $A\rightarrow\Delta S\rightarrow B$ )
Requirement Perspective	Surface issue labels (Bug, Feature, Enhancement)	Task-specific tests and patch evaluation	Ontological taxonomy (ISO 14764 / 25010) with verified quotes
Oracle Evaluation Scope	Accepts existing benchmark test harnesses as ground truth	Augments benchmark tests to flag incorrect passes	Secondary case-level assertion inspection
Empirical Scope	SWE-bench Lite/Verified (Python repository graphs)	SWE-bench Python repos (LLM-augmented test suites)	Analyzed AI tasks ( $N=944$ ) vs. Fineract issues ( $N=2,925$ )

Table 1: Research positioning by unit of analysis and primary contribution.

Panel of Figure 1 — Figure 1: Three complementary lenses for multi-condition fulfillment: breadth, co-occurrence, and source-grounded directionality. The illustrated dependency chain synthesizes FINERACT-753 and FINERACT-2338 rather than quoting a single task; oracle inspection is a downstream application.

Code	Codebook Criterion Name	AI All ( $N=944$ )	Pro ( $N=731$ )	Poly ( $N=165$ )	EVO ( $N=48$ )
A1	Product Operation & Business Rules	426 (45.13%)	300 (41.04%)	84 (50.91%)	42 (87.50%)
A2	Structure & Implementation Method	572 (60.59%)	505 (69.08%)	50 (30.30%)	17 (35.42%)
A3	Testing & Quality Assurance	94 (9.96%)	70 (9.58%)	10 (6.06%)	14 (29.17%)
A4	Execution & Operations	122 (12.92%)	93 (12.72%)	20 (12.12%)	9 (18.75%)
A5	Deployment & Environment Migration	60 (6.36%)	39 (5.34%)	9 (5.45%)	12 (25.00%)
A6	Development Process & Docs	53 (5.61%)	25 (3.42%)	17 (10.30%)	11 (22.92%)

Table 2: Stratified 18-Criteria Distribution Across Benchmark Families (

N=944

Metric / Bin	Fineract ( $N=2,925$ )	AI All ( $N=944$ )	Pro ( $N=731$ )	Poly ( $N=165$ )	EVO ( $N=48$ )
Mean $\|B\|$	1.78 (1.7798)	2.24 (2.2447)	2.47 (2.4733)	1.22 (1.2182)	2.29 (2.2917)
Median $\|B\|$	2.0	2.0	2.0	1.0	2.0
$\|B\|=0$	331 (11.32%)	3 (0.32%)	0 (0.00%)	3 (1.82%)	0 (0.00%)
$\|B\|=1$	872 (29.81%)	248 (26.27%)	109 (14.91%)	126 (76.36%)	13 (27.08%)
$\|B\|=2$	1,097 (37.50%)	357 (37.82%)	304 (41.59%)	33 (20.00%)	20 (41.67%)
$\|B\|=3$	451 (15.42%)	231 (24.47%)	219 (29.96%)	3 (1.82%)	9 (18.75%)

Table 3: Fulfillment-Condition Breadth: Distribution of

|B|

(not a measure of dependency strength).

Pair	Condition Pair (non-directional)	Fineract ( $N=2,925$ )	AI Bench ( $N=944$ )
		Co-occur / $\phi$	Co-occur / $\phi$ ( $p$ )
$B_{4}\leftrightarrow B_{2}$	Temporal / Numerical	254 / $\mathbf{+0.430}$	5 / $\mathbf{-0.013}$ ( $0.825$ )
$A_{1}\leftrightarrow B_{4}$	Business Rule / Temporal	471 / $\mathbf{+0.309}$	42 / $\mathbf{+0.024}$ ( $0.496$ )
$A_{1}\leftrightarrow B_{2}$	Business Rule / Numerical	440 / $\mathbf{+0.343}$	34 / $\mathbf{+0.039}$ ( $0.246$ )
$A_{1}\leftrightarrow B_{3}$	Business Rule / Consistency	538 / $\mathbf{+0.329}$	115 / $\mathbf{+0.094}$ ( $0.005$ )
$B_{4}\leftrightarrow B_{3}$	Temporal / Consistency	138 / $\mathbf{+0.069}$	22 / $\mathbf{+0.022}$ ( $0.501$ )

Table 4: Pairwise Co-occurrence (

\phi

); associations do not establish causal direction.

ID	Issue	Classification	Structural Chain	Verbatim Jira Excerpt
Causal Cascade Focus: Multi-Tier Loan Modification (FINERACT-2338)
DEP-01	FINERACT-2338	Explicit Dependency	$A_{1}\rightarrow B_{4}$	“Backdate can be done on already paid Installments as well”
DEP-02	FINERACT-2338	Explicit Dependency	$B_{4}\rightarrow B_{2}$	“Repayment schedule will be recalculated with New EMI and Interest from the Interest applied schedule date”
DEP-03	FINERACT-2338	Explicit Dependency	$B_{2}\rightarrow B_{6}$	“No of Installments will remain the same, only EMI and Interest would get affected”
DEP-04	FINERACT-2338	Explicit Dependency	$B_{4}\rightarrow B_{3}$	“standard accounting rules and not charge-off accounting rules”
Cross-Entity Invariant Preservation & Transaction Isolation

Table 5: Representative Source-Grounded Requirement Dependencies in Apache Fineract Maintenance (Full set of 16 documented relationships in the replication package).

Axis / Task	Specification Mandate ( $R_{\text{spec}}$ )	Evaluation Test Oracle ( $R_{\text{eval}}$ )	Observed Scope	Construct Alignment or Gap Analysis
Axis 1 (Direct) NodeBB #0030	Explicit ( $A_{5}\rightarrow B_{6}$ ) : DB migration script ( chat_allow_list.js , 44 LOC) migrating legacy restrictChat records.	Leaf Unit Assertion : Asserts canMessageUser() on mock objects in memory ( test/messaging.js , L103–121).	In-memory leaf	Construct Underrepresentation : Direct test patch asserts leaf permissions; DB migration script unasserted in analyzed test patch.
Axis 2 (Scope) Element-Web #0139	Explicit badge update; inferred cache persistence : Recalculate unread badges; IndexedDB cache synchronization is inferred rather than explicitly mandated.	Mocked State : Asserts helper return values under synthetic event mocks ( test/Unread-test.ts ).	In-memory mock	Mocked Logic vs. Persistent Cache : In-memory transitions verified; asynchronous IndexedDB cache persistence outside evaluation scope.
Axis 3 (Hierarchy) RocketMQ #0819	Explicit ( $B_{7},B_{2}$ ) : Thread-safe counter insertion ( StatsItemSet , 20kL) under JVM thread contention.	Thread Stress Test : 100 worker threads executing 10,000 tasks ( StatsItemSetTest.java ).	JVM thread contention	Single JVM Concurrency Met : Checks concurrent counter accumulation within a single JVM; distributed transaction boundaries are not mandated by the task.
Axis 4 (Aligned) Navidrome #0495	Explicit ( $A_{4},B_{6},B_{10}$ ) : Online SQLite DB backup, destructive DDL recovery, and retention pruning.	Integrated Engine Test : Executes backup, runs destructive DDL, restores DB, asserts pruning ( db/backup_test.go ).	Persistent DB state	Aligned Invariant Testing : Positive case in which the inspected tests directly address the explicitly required persistent-state operations.

Table 6: Comparative Case Analysis: Test Oracle Construct Coverage Across Four Qualitative Benchmark Axes (Unabridged matrix in replication package).

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Benchmarks drive AI progress, but are hampered by task under-specification and over-specification. Task under-specification causes guesswork, where solutions have to make things up, or worse, resort to contamination or reward hacking. Task over-specification causes spoilers, where solutions can bypass some of the harder steps the benchmark was supposed to measure. Since future-looking benchmarks should be difficult, over-specification reduces a benchmark's shelflife of community adoption. Repository-level coding benchmarks have helped the field progress but experience unnecessary churn due to both over- and under-specification. For instance, SWE-bench Pro, FEA-bench, Doc2Feat-bench, and Multi-SWE-bench all tackle under-specification by augmenting task statements with hints derived from ground truth. We show that leaking ground-truth information into the task statement causes over-specification in some of these benchmarks. We experiment with various specification levels, trying to thread the needle between spoilers and guesswork. Based on these results, we recommend techniques for deriving parsimonious hints. We will release our hints so the community can obtain more reliable signals from popular benchmarks.

Abstract

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