Alibaba Cloud Software / SaaS China Translated from Chinese

AsiaInfo Technologies (亚信科技)

Application Refactoring · IT Operations Automation

AsiaInfo had to raise software-engineering productivity while maintaining large, long-lived telecom BSS and OSS systems whose logic spanned multiple generations from 2G to 5G. The codebase and architecture were highly coupled, decades of expert know-how were scattered across senior engineers, documents, and unsynchronized systems, and new staff needed months to understand even a single module, which made it hard to support fast business iteration.

average end-to-end efficiency from requirements to testing improved 8.6%
code output per engineer increased about 14.6%
monthly active rate stayed above 90% in a 1,000-person operations team
overall AI coverage of core BSS systems reached about 80%

Reported impact

Business outcome

After a three-year AI transformation, AsiaInfo brought nearly ten thousand R&D staff into an AI-coding operating model, lifted code output per engineer about 14.6%, improved average end-to-end efficiency from requirements through testing 8.6%, reached about 80% AI coverage across core BSS systems, and maintained more than 90% monthly activity in a 1,000-person operations team while turning senior experts' tacit know-how into reusable team capability.

  • Productivity Improvement
  • Speed to Market

Solution

AsiaInfo moved its engineering organization toward an AI-native delivery model by deploying Alibaba Cloud Qoder across requirements, design, coding, testing, and defect repair for complex telecom BSS and OSS systems. Qoder's knowledge engine ingests historical code, API assets, architecture characteristics, and development conversations, extracting norms and experience into searchable knowledge cards so expertise no longer stays trapped with senior specialists. Engineers and AI agents retrieve those cards during work, while human architects use Spec to define module boundaries, interface contracts, and agent role constraints. In complex defect scenarios, Qoder splits work across agents for fault localization, repair, and verification that share context and recompose results at the architecture layer. The platform acts as both an enterprise knowledge layer and a multi-agent coding workflow, turning decades of expert know-how into reusable team capability across a large R&D organization.

Data flow

Historical code, API assets, architecture features, and ongoing development conversations are ingested into Qoder's knowledge engine, which turns them into searchable knowledge cards. Engineers and AI agents retrieve those cards during requirements, design, coding, testing, and defect repair; for complex defects, architects define module and role constraints with Spec, work is split across localization, repair, and verification agents, and the results are recomposed into the BSS/OSS development flow.

Solution architecture

2 components · 2 layers
  1. Orchestration
    • Qoder Provides the knowledge engine, code assistance, and multi-agent workflow used across AsiaInfo's AI-native R&D process.
  2. Uncategorized
    • Qoder CN Represents the enterprise Qoder edition listed for AsiaInfo's governed, large-team AI coding rollout.

Architecture clues

  • Architects use Spec to define module boundaries, interface contracts, and role constraints before agents execute complex tasks.
  • Defect work is parallelized across specialized agents for localization, repair, and verification, with composition happening back at the architecture layer.
  • Knowledge cards are shared across human engineers and AI agents so expertise can be retrieved and reused in real time instead of remaining with individual specialists.
  • Qoder's knowledge engine automatically extracts rules and experience from historical code, API assets, architecture characteristics, and development conversations into reusable knowledge cards.

Evidence from the source

AI can automatically extract rules and knowledge from historical code, API assets, and architecture characteristics, then preserve daily development conversations as knowledge cards. (original: AI 能够从海量历史代码、API 资产和架构特征中自动提取规范和知识,并将日常研发对话沉淀为知识卡片。)
After three years of AI transformation, AsiaInfo brought nearly ten thousand R&D staff into an AI Coding era and turned senior experts' tacit experience into reusable team capability. (original: 通过为期三年的 AI 转型实践,亚信科技成功将近万名研发人员带入“全员 AI Coding”时代。Qoder 的引入,不仅将老专家的隐性经验转化为可复用的团队能力。)
AsiaInfo said its BSS and OSS systems span multiple generations from 2G to 5G, with intertwined old and new logic and high module coupling that makes R&D and maintenance extremely difficult. (original: 系统横跨从 2G 到 5G 的多个技术代际,新老业务逻辑交织,模块间耦合度高,“牵一发而动全身”,研发与维护难度极大。)
Average code output per engineer increased by about 14.6%, and average end-to-end efficiency from requirements through testing improved 8.6%. (original: 人均代码产出提升约14.6%;从需求到测试的端到端全流程平均提效8.6%。)
For complex defect work, AsiaInfo uses Qoder to run multiple agents in parallel for localization, repair, and verification while they share context and are recomposed at the architecture layer. (original: 将复杂的业务需求拆解给缺陷定位、修复、验证等多个 Agent 并发执行。Agent 之间共享上下文并互相协同,最后在架构层进行合成。)
Overall AI coverage of core BSS systems reached about 80%, and monthly active rate stayed above 90% in a 1,000-person operations team. (original: 核心 BSS 系统 AI 覆盖率整体达到80%左右;在千人规模的运营团队中,月活跃率稳定90%以上。)
Qoder was rolled out across the full R&D system, not just code completion, progressively covering requirements, design, testing, and defect repair. (original: 通过 Qoder,亚信科技不仅实现了代码补全,更逐步覆盖了需求、设计、测试、缺陷修复等完整的研发体系。)
Transparency Evidence & methodology 95/100 reference value · 95% confidence · 2 sources

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value95/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    4 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence95%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability25/25
    12 of 12 key fields link to a named source.
  • Direct evidence20/20
    7 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency10/10
    2 source references document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Alibaba Cloud

    亚信科技xQoder,让30年沉淀从“专家经验”转化为“团队能力”_互联网与信息技术_阿里云客户案例

    First-party
    https://www.aliyun.com/customer-stories/internet-and-info-tech-2026-asiainfo
    Source type
    Vendor-published customer story
    Supports
    • Customer
    • Company size
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
  2. AsiaInfo Technologies

    亚信科技-数智化全栈能力提供商

    First-party
    https://www.asiainfo.com/zh_cn/index.html

    The homepage HTML states 总部地址:北京市海淀区中关村软件园二期, placing the headquarters in Beijing, China.

    Source type
    Official company website
    Supports
    • Region

Date evidence

  • Published on source page Jun 9, 2026 · Alibaba Cloud · First-party
    Open date source
Not separately sourced Industry. Treat these normalized attributes as lower-confidence until a dedicated source is added.