Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead

Haoyu Wang; Hongzhou Rao; Shenao Wang; Xinyi Hou; Yanjie Zhao

REVIEW 3 major objections 8 minor 300 references

Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead

T0 review · 3 major / 8 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read This paper argues that LLM development is best understood as a software-engineering problem spanning six lifecycle phases, and that treating it this way reveals where established SE practice transfers and where new methods are needed.

desk verdict A useful six-phase organizing survey whose 'first comprehensive' claim outruns its undocumented methodology; referee it, but require the novelty claim to be fixed. read the letter →

arxiv 2506.23762 v1 pith:LHAE4NGN submitted 2025-06-30 cs.SE cs.AI

Hongzhou Rao , Yanjie Zhao , Xinyi Hou , Shenao Wang , Haoyu Wang This is my paper

classification cs.SEcs.AI

keywords largelanguagemodelssoftwareengineeringLLMlifecyclerequirementsdatasetconstructiontestingandevaluationdeploymentoperationsmaintenanceevolution

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper sets out to establish that LLM development is, at bottom, a software-engineering problem, and that the field is ready for a life-cycle-wide treatment. It divides the LLM lifecycle into six phases—requirements engineering, dataset construction, model development and enhancement, testing and evaluation, deployment and operations, and maintenance and evolution—and surveys the research status of each, followed by the key challenges and proposed research directions. The intended payoff is a shared map: researchers and practitioners can see which software-engineering practices transfer directly to LLMs, which need new methods, and where the open problems concentrate. If the framing holds, it would give the community a common structure for comparing results across phases and for steering future research toward the least-engineered parts of the lifecycle.

What carries the argument

The load-bearing object is the six-phase lifecycle taxonomy, which splits LLM engineering into requirements engineering, dataset construction, model development and enhancement, testing and evaluation, deployment and operations, and maintenance and evolution. The taxonomy is what turns a large collection of related papers into a structured argument: every phase receives a research-status review, a challenge list, and a 'road ahead' section, and the phases are compared against traditional software on dimensions such as determinism, testability, maintainability, and deployability to decide where software-engineering practice transfers and where it must be invented.

What would settle it

Re-running the paper's literature collection with a documented search protocol would settle the core claim: if a peer-reviewed survey already covers all six lifecycle phases through a software-engineering lens, or if a phase contains a substantial body of software-engineering-for-LLM work that the paper never mentions, the 'first comprehensive' claim and the roadmap's completeness would be refuted.

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Extended reading notes

Core claim

The paper's central claim is that no existing research has systematically analyzed the LLM development lifecycle through the lens of software engineering, and that doing so yields a usable six-phase organization: requirements engineering, dataset construction, model development and enhancement, testing and evaluation, deployment and operations, and maintenance and evolution. For each phase the paper catalogs current research, names the dominant challenges (for example, defining requirements for non-deterministic systems, data poisoning and authorization, catastrophic forgetting, benchmark contamination, deployment latency and energy costs, and technical debt and model drift), and proposes directions it believes are promising. It also argues that LLMs sit between traditional software and classical machine-learning systems—they keep maintainability, reusability, and scalability characteristics but lose determinism and easy testability—so software-engineering methods need selective adaptation rather than wholesale transfer. The paper positions itself as the first comprehensive survey of this intersection.

Load-bearing premise

The value of the survey depends on its claim to be the first comprehensive software-engineering treatment of the whole LLM lifecycle, but it does not describe how the reviewed papers were searched for, selected, or screened, so the reader cannot verify that the coverage is systematic or unbiased.

Editorial extensions

If this is right

If the six-phase framing is adopted, requirements engineering for LLMs must move beyond treating LLMs as components of AI systems and start defining measurable functional and non-functional requirements for models themselves, including accuracy, latency, explainability, and acceptable trade-offs.
Dataset construction becomes an identifiable engineering discipline with its own open problems—poisoning defense, provenance, membership inference, watermarking, and bias-aware diversity scoring—rather than a data-cleaning side task.
Testing and evaluation of LLMs needs dedicated infrastructure: contamination-resistant benchmarks, adaptive benchmark management, and robust LLM-as-a-judge frameworks with explicit bias mitigation.
Deployment and operations split into cluster, edge, and hybrid modes, so research should target each mode's specific constraints—scheduling and energy in clusters, compression and platform heterogeneity on edge devices, and communication and data security in edge-cloud collaboration.
Maintenance and evolution deserves systematic study in its own right, with tools for technical-debt measurement, drift detection beyond full retraining, and automated ethical and legal compliance during model updates.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the lifecycle taxonomy catches on, it becomes a shared coordinate system: any new LLM technique could be classified by phase, and progress across phases could be compared directly; the paper stops short of proposing such a classification scheme for future work.
Because the paper repeatedly stresses LLMs' probabilistic outputs, it points toward a research program it does not name: adapting classical statistical and metamorphic testing techniques to model behavior, which would give the testing and evaluation phase a firmer technical foundation.
The same six-phase lens could be applied specifically to LLM agents and multi-agent systems, where tool integration, inter-agent protocols, and routing cut across all six phases; that application is left for readers to develop.
A testable extension follows from the weakest part of the evidence: re-running the survey with an explicit, reproducible search protocol would show whether the six phase-challenge lists are complete or whether some phases are sparse only because of how the literature was gathered.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Senior colleague, here's my take on arXiv:2506.23762.

The paper's real contribution is a usable organizing frame: six phases of the LLM lifecycle (requirements, data, development, testing, deployment, maintenance), each with a research-status summary, challenge list, and future directions. That structure is genuinely useful and the coverage is broad; someone entering this area would get a decent map. The authors also do a good job of connecting SE concerns (versioning, monitoring, security, technical debt) to LLM-specific issues.

The soft spot is the 'first comprehensive survey' claim. It's in the abstract and Section 9, and it's a global negative that needs support. The paper never documents a search strategy, inclusion/exclusion criteria, or screening process, so the reader can't verify the claim. Worse, the related-work section itself cites earlier surveys on LLMs for SE (Hou et al. [126], Fan et al. [78], Liu et al. [210]) and on evaluation/security, and the authors borrow existing taxonomies (model compression from [29,206,470], PEFT from [114,389]). That doesn't make the survey bad, but it makes 'first comprehensive' hard to defend. The honest fix is to either soften the claim to 'a survey organized around a six-phase SE lifecycle' or add a methodology section and a scope-comparison table against prior surveys. I'd want one of those before acceptance.

The rest of the concerns are minor: some typos ('we presents'), a few sections lean on a small number of examples, and the 'road ahead' items are necessarily speculative. None of that is load-bearing.

Verdict: it deserves peer review. A serious referee can push on the novelty claim and the missing methodology, and the paper will be stronger for it. I'd cite it as a recent organizing survey, though not as 'the first.'

Referee Report

3 major / 8 minor

Summary. This is a narrative survey of software-engineering (SE) challenges across the full lifecycle of large language models (LLMs). The lifecycle is partitioned into six phases—requirements engineering, dataset construction, model development and enhancement, testing and evaluation, deployment and operations, and maintenance and evolution—and each phase is treated in three parts: research status, key challenges, and future research directions. The paper draws on roughly 476 references, weighted toward 2023–2025 work, includes recent ecosystem developments such as MCP and A2A protocols, and claims in the Abstract, §1, and §9 that it is the first comprehensive survey to examine the LLM development lifecycle through the lens of SE. No experiments or derivations are presented; the paper's contribution is entirely organizational and interpretive.

Significance. The paper's intended contribution is organizational: a six-phase lifecycle frame with per-phase challenge and direction summaries, and with security concerns integrated into each phase rather than isolated in a single security chapter. If the coverage claim holds, this is a useful roadmap for a fast-moving area, and the paper is unusually current, discussing MCP, A2A, alignment faking, and LLMOps from 2024–2025 literature. The manuscript deserves credit for explicitly attributing its subsection taxonomies to prior surveys (§5.4, §5.5) and for citing the authors' own earlier SLR rather than hiding it. The significance is nevertheless conditional: the headline novelty claim cannot be verified as submitted, because no literature-search methodology is documented and the related-work section does not perform a scope comparison against adjacent surveys.

major comments (3)

[Abstract, §1, §9] The paper's central claim—that no prior work systematically explores LLM development from a software-engineering perspective and that this is 'the first comprehensive survey'—is a global claim about the published literature, yet the manuscript documents no methodology to support it. There is no description of search strategy, bibliographic databases, time window, inclusion/exclusion criteria, screening process, or quality assessment, so a reader cannot verify that the coverage is comprehensive, representative, or unbiased. This is load-bearing because the novelty claim is stated unqualified in the Abstract ('no existing research systematically explores') and is listed as the first contribution in §1. Please add a methodology subsection documenting the search and screening protocol, or substantially soften the Abstract's unqualified global negative to match the qualified 'to the best of our knowledge' wording already used in §9; ideally, do both and support the choice with a scope-comparison table of adjacent surveys.
[§9] The related-work section inventories existing surveys but never compares their scope against the proposed six-phase taxonomy, so the claimed gap is asserted rather than demonstrated. The relationship to the authors' own prior systematic literature review [126] is dismissed in a single sentence, without stating the governing distinction between LLMs-for-SE (the topic of [126]) and SE-for-LLMs (the topic of this paper). Likewise, the LLMOps lifecycle survey [59] and the RE4AI mapping study [8] are cited, but no explicit differentiation shows why their lifecycle or requirements coverage does not overlap with the deployment/operations and requirements phases of this survey. Please add a structured comparison (e.g., a table mapping each adjacent survey to the six phases) and state precisely which phases and SE aspects are new in this paper.
[§5.4, §5.5] The manuscript honestly attributes its subsection taxonomies ('the same as [29,206,470]' for model compression and 'the same as [114,389]' for PEFT), but this transparency exposes an opacity at the level of the paper's central frame: a reader cannot tell whether the six-phase lifecycle decomposition emerged inductively from the reviewed corpus or was imposed a priori, and the manuscript does not state which is the case. Since the six-phase division is the paper's second stated contribution in §1, its provenance is load-bearing. Please state explicitly how the phases were derived (e.g., inductively from the literature, or adapted from prior lifecycle frameworks such as [59]), and mention any candidate phases that were considered and excluded.

minor comments (8)

[§9] 'we presents the first comprehensive survey' is a grammatical error and should read 'we present the first comprehensive survey.'
[§1] 'Micros ft's DeepSpeed' appears to be a broken rendering of 'Microsoft's DeepSpeed'; please fix the formatting at the line break.
[§5.2.3] 'making it easier for LLMser to switch between' contains the typo 'LLMser'; it should be 'LLM users' (or 'users').
[§7.1.1] 'the imperfect of LLM deployment frameworks introduce further security concerns' is ungrammatical; it should read 'the imperfections of LLM deployment frameworks.'
[§5.5.1] The term 'soft prompt' is used inconsistently ('soft prompt avoid additional structural complexity', 'soft prompt are vulnerable to adversarial attacks'); please use the standard plural 'soft prompts' throughout.
[§2.2.2] The closing sentence of the first paragraph ('Additionally, the development of modular and standardized LLM service interfaces (e.g., OpenAI API [268])...') is nearly duplicated at the end of the next paragraph; please delete one occurrence.
[§1] 'extensive research has explored LLM capabilities [126,380,471]' cites the authors' own SLR [126], which is a survey of LLMs for software engineering rather than of LLM capabilities; please correct the characterization or the citation.
[References] Several factual claims about tool and system behavior rest on non-peer-reviewed preprints (e.g., [123], [284]); for the journal version, please prefer peer-reviewed sources or clearly mark preprint status in the bibliography.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: this is a literature survey with no derivation chain, no fitted parameters, and no predictions; its self-citations are ordinary evidence, and the unsupported 'first comprehensive survey' claim is a verifiability issue, not a circular one.

full rationale

This paper is a survey, not a derivation: it contains no equations, fits no parameters, and makes no quantitative prediction whose output could reduce to its input. The six-phase lifecycle taxonomy (requirements engineering, dataset construction, model development and enhancement, testing and evaluation, deployment and operations, maintenance and evolution) is an organizing frame imposed by the authors, not a result derived from their own prior work; the paper never claims to derive the phases from anything, so there is no self-definitional step. Where sub-taxonomies are adopted, they are explicitly borrowed from external surveys ('We categorize it into three main approaches: quantization, KD, and pruning, the same as [29, 206, 470]' in §5.4; 'We categorize PEFT methods into three main types: additive, reparameterized, and selective approaches, the same as [114, 389]' in §5.5), which is ordinary citation of prior work, not circular import. The paper's self-citations ([125] on LLM deployment framework vulnerabilities, [126] on an SLR of LLMs for SE, [127] on MCP security) are used as evidence for specific empirical claims and are not the justification for the survey's conclusions or for its taxonomy; none of them is invoked as a uniqueness theorem or as a forced choice, so the 'uniqueness imported from authors' and 'ansatz smuggled via citation' patterns do not apply. The central novelty claim — 'To the best of our knowledge, we presents the first comprehensive survey in this work that examines the LLM development lifecycle through the lens of SE' (§9) — is a global negative about the literature that the paper does not substantiate with a documented search strategy, inclusion criteria, or screening process; the skeptic's load-bearing attack targets exactly this missing methodology. That is a real verifiability and correctness weakness, but it is not circularity: the claim does not reduce to the paper's own definitions, fitted values, or self-citations, and a false or unverified novelty claim is a factual-evidence problem rather than a construction-level equivalence. No step was found in which an output equals an input by construction, so the honest finding is no significant circularity (score 0).

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper is a survey, so it introduces no fitted parameters and no physical or conceptual entities that require independent evidence. The proposed 'intelligent prompt and secure framework' in Section 5.3.3 is a suggested future direction, not a postulated entity. The main implicit assumptions are the validity of the six-phase taxonomy, the representativeness of the literature, and the transferability of SE principles.

assumptions (3)

ad hoc to paper The six-phase lifecycle taxonomy (requirements, dataset, development, testing, deployment, maintenance) is a valid and complete decomposition of LLM development from an SE perspective.
The paper asserts this division in Section 1 and Figure 1 without deriving it from a corpus analysis or a prior framework. The taxonomy is the paper's own organizational scheme.
domain assumption The cited literature is representative of the field despite the absence of a documented search and selection method.
No methodology section describes search queries, inclusion criteria, or screening. The survey's conclusions about research status rest on this unstated assumption.
domain assumption Traditional software engineering principles can be transferred to LLMs despite their probabilistic and opaque nature.
The entire paper frames LLM development as amenable to SE methods, based on the comparison table in Section 2 showing both similarities and differences. This assumption is asserted rather than proven.

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Cite this review

Pith. "Pith review of Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead." pith.science (2026). https://pith.science/paper/LHAE4NGN

@misc{pith2026250623762,
  author       = {Pith},
  title        = {Pith review of: Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LHAE4NGN}},
  note         = {Machine review of arXiv:2506.23762}
}

read the original abstract

The rapid advancement of large language models (LLMs) has redefined artificial intelligence (AI), pushing the boundaries of AI research and enabling unbounded possibilities for both academia and the industry. However, LLM development faces increasingly complex challenges throughout its lifecycle, yet no existing research systematically explores these challenges and solutions from the perspective of software engineering (SE) approaches. To fill the gap, we systematically analyze research status throughout the LLM development lifecycle, divided into six phases: requirements engineering, dataset construction, model development and enhancement, testing and evaluation, deployment and operations, and maintenance and evolution. We then conclude by identifying the key challenges for each phase and presenting potential research directions to address these challenges. In general, we provide valuable insights from an SE perspective to facilitate future advances in LLM development.

Figures

Figures reproduced from arXiv: 2506.23762 by the authors.

**Figure 2.** Detailed Activity Breakdown for the LLM Development Lifecycle. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Challenges and Road Ahead in §3 Requirements Engineering. 3.1 Research Status To the best of our knowledge, research on RE for LLMs remains relatively limited. Due to the strong natural language processing (NLP) capabilities of LLMs, existing studies primarily focus on leveraging LLMs to support RE tasks, while comparatively fewer efforts investigate RE methodologies for LLM development itself. This imbalance mirro… view at source ↗

Figures from the paper (6 more)

**Figure 4.** Figure 4: Challenges and Road Ahead in §4 Dataset Construction. According to the construction method, we broadly categorize current approaches to improving data quality into two main strategies: (a) manual data labeling and rule-based selection, and (b) LLMassisted data constru…

**Figure 5.** Figure 5: Challenges and Road Ahead in §5 Development and Enhancement. effectiveness of pre-training has been a long-standing research focus, with efforts directed towards optimizing training datasets, refining training methodologies, and improving computational efficiency [197…

**Figure 6.** Figure 6: Overlap between LLM-based agents, multimodal models, and multi-model collaboration. [PITH_FULL_IMAGE:figures/full_fig_p018_6.png]

**Figure 7.** Figure 7: Challenges and Road Ahead in §6 Testing and Evaluation. , Vol. 1, No. 1, Article . Publication date: August 2025 [PITH_FULL_IMAGE:figures/full_fig_p028_7.png]

**Figure 8.** Figure 8: Challenges and Road Ahead in §7 Deployment and Operations. 7.1 Cluster Deployment 7.1.1 Research Status. Cluster deployment refers to deploying models in high-performance computing clusters, such as the cloud, to leverage distributed computing for large-scale inferenc…

**Figure 9.** Figure 9: Challenges and Road Ahead in §8 Maintenance and Evolution. The rapid advancement of LLMs often leads to the accumulation of technical debt, as adhoc solutions (e.g., memory management, model compression, and attention optimization) are implemented to address short-ter…

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