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引言

隨著現(xiàn)代計算機時代的到來，自然語言與機器語言之間的鴻溝逐漸縮小。硬件與軟件的進步使得編程經(jīng)歷了多次演變，而最近人工智能（AI）和高性能計算的飛速發(fā)展更是讓這一鴻溝幾乎消失。通過利用大型語言模型（LLMs），生成式AI（Generative AI）正在顯著提升軟件開發(fā)者的生產(chǎn)力、軟件質(zhì)量和市場投放速度。本文探討了生成式AI在軟件工程領(lǐng)域的應(yīng)用、挑戰(zhàn)及前景。

主要發(fā)現(xiàn)與益處

1. 創(chuàng)新與軟件質(zhì)量提升

• 創(chuàng)新驅(qū)動力：61%的受訪組織認為，生成式AI在軟件工程中的最大益處是促進了創(chuàng)新工作，如開發(fā)新軟件功能和服務(wù)。通過自動化重復(fù)性任務(wù)，生成式AI使開發(fā)者有更多時間專注于創(chuàng)新和增值任務(wù)，從而激發(fā)更大的創(chuàng)造力。
• 軟件質(zhì)量提升：49%的受訪組織表示，生成式AI的使用提高了軟件質(zhì)量。例如，通過提供代碼建議，生成式AI可以減少錯誤并增強測試覆蓋率，從而提升整體軟件質(zhì)量。

2. 生產(chǎn)力提升

• 早期估算顯示，使用生成式AI的組織在軟件工程功能上的生產(chǎn)力提高了7%至18%。特別是在編碼輔助和文檔編寫等特定任務(wù)中，最大潛力分別達到34%和35%的時間節(jié)省，平均分別為9%和10%。
• 大多數(shù)組織將這些生產(chǎn)力增益用于創(chuàng)新工作（50%）和員工技能提升（47%），而非減少員工數(shù)量（4%）。

3. 員工滿意度與協(xié)作

• 高達69%的高級軟件專業(yè)人員和55%的初級軟件專業(yè)人員表示，對使用生成式AI進行軟件開發(fā)感到高度滿意。
• 78%的軟件專業(yè)人員對生成式AI在增強業(yè)務(wù)和技術(shù)團隊之間協(xié)作方面的潛力持樂觀態(tài)度。

采納現(xiàn)狀與未來趨勢

1. 采納階段

• 目前，生成式AI在軟件工程中的采納仍處于早期階段，90%的組織尚未實現(xiàn)規(guī)?；瘧?yīng)用。
• 27%的組織正在進行生成式AI試點，11%的組織已經(jīng)開始在軟件功能中利用生成式AI。
• 大型組織（年收入超過200億美元）的采納率顯著高于小型組織（年收入在1至50億美元之間），前者75%已采納或試點，后者僅為23%。

2. 未來展望

• 預(yù)計未來兩年內(nèi)，使用生成式AI工具的軟件工作者比例將從目前的46%顯著增長至85%。
• 到2026年，生成式AI預(yù)計將協(xié)助完成超過25%的軟件設(shè)計、開發(fā)和測試工作。

挑戰(zhàn)與風(fēng)險

1. 基礎(chǔ)條件不足

• 僅27%的組織擁有實施生成式AI所需的平臺和工具，32%的組織具備人才基礎(chǔ)。
• 超過60%的組織缺乏針對生成式AI的軟件工程治理和培訓(xùn)計劃。

2. 非正式使用風(fēng)險

• 63%的軟件專業(yè)人員使用未經(jīng)授權(quán)的生成式AI工具，這可能導(dǎo)致功能問題、安全漏洞和法律風(fēng)險，如代碼泄露和知識產(chǎn)權(quán)問題。
• 近三分之一的員工通過自學(xué)生成式AI技術(shù)，而少于40%的員工獲得了組織提供的培訓(xùn)。

實現(xiàn)潛力的策略

1. 選擇并優(yōu)先實施高收益用例

• 組織應(yīng)識別并優(yōu)先實施那些能夠帶來最大效益的生成式AI用例，如編碼輔助、測試案例生成、文檔編寫等。

2. 風(fēng)險管理

• 制定全面的風(fēng)險管理策略，以減輕安全、知識產(chǎn)權(quán)和代碼泄露等方面的風(fēng)險。

3. 組織轉(zhuǎn)型

• 引入生成式AI助手以增強軟件團隊，并準備相應(yīng)的技術(shù)前提條件，如建立平臺和工具庫。
• 創(chuàng)建一個學(xué)習(xí)文化，為員工提供培訓(xùn)和跨技能培訓(xùn)機會，以支持生成式AI的采納和使用。

4. 監(jiān)測與優(yōu)化

• 采用測量協(xié)議來監(jiān)測生成式AI的影響，并根據(jù)實際反饋調(diào)整和優(yōu)化用例的優(yōu)先級。

結(jié)論

生成式AI正在改變軟件工程的面貌，通過提升創(chuàng)新力、軟件質(zhì)量和生產(chǎn)力，為組織帶來顯著優(yōu)勢。然而，其采納過程中也伴隨著諸多挑戰(zhàn)和風(fēng)險，需要組織制定全面的策略來應(yīng)對。通過選擇合適的用例、強化風(fēng)險管理、推動組織轉(zhuǎn)型和持續(xù)優(yōu)化，組織可以最大限度地發(fā)揮生成式AI在軟件工程中的潛力。對于技術(shù)、IT、產(chǎn)品、戰(zhàn)略、研發(fā)/工程、一般管理和創(chuàng)新領(lǐng)域的業(yè)務(wù)領(lǐng)導(dǎo)者而言，這一報告提供了寶貴的見解和實施指南。

An Analysis of the Capgemini 2024 Generative AI in Software Engineering Report

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Introduction

The advent of generative artificial intelligence (AI) has disrupted the landscape of software engineering, ushering in a new era of automation, productivity, and innovation. The Capgemini Research Institute’s 2024 report, “Turbocharging software with Gen AI: How organizations can realize the full potential of generative AI for software engineering,” delves into the various aspects of generative AI’s impact on the software development lifecycle (SDLC). This article will summarize the key findings, benefits, challenges, and strategies outlined in the report, offering insights into how organizations can harness the full potential of generative AI for software engineering.

Key Findings and Benefits

Innovation and Software Quality Enhancement

One of the most prominent benefits of generative AI in software engineering is its ability to augment innovative work. According to the report, 61% of surveyed organizations cite enabling more innovative work, such as developing new software features and services, as the primary advantage of generative AI. This underscores the potential of generative AI to empower developers to focus on value-added tasks rather than time-consuming, repetitive work.

Moreover, 49% of organizations report improved software quality as a significant benefit. Generative AI tools, through code suggestions, error detection, and enhanced testing capabilities, contribute to reducing bugs and enhancing overall software quality.

Productivity Boost

Early estimates indicate that organizations leveraging generative AI have seen a 7% to 18% productivity improvement in software engineering functions. This gain is particularly evident in specialized tasks like coding assistance (with a maximum potential of 34% time savings and an average of 9%) and documentation creation (35% maximum potential with 10% average savings). These productivity gains are then utilized for further innovative work (50%) and employee upskilling (47%), with only 4% of organizations aiming to reduce headcount.

Employee Satisfaction and Collaboration

Generative AI is also positively impacting software professionals’ job satisfaction. The report reveals that 69% of senior software professionals and 55% of junior professionals report high levels of satisfaction from using generative AI for software engineering. Additionally, 78% of software professionals are optimistic about generative AI’s potential to enhance collaboration between business and technology teams.

Adoption Status and Future Trends

Early Adoption Stage

Currently, generative AI adoption in software engineering is still in its early stages, with 9 in 10 organizations yet to scale their implementation. Only 27% of organizations are running generative AI pilots, and 11% have started leveraging it in their software functions. Notably, large organizations (annual revenue > 20billion)areleadingtheway,with751-5 billion).

Accelerating Adoption

The report projects a significant increase in adoption over the next two years. Currently, 46% of the software workforce uses generative AI tools for various purposes (training, experimenting, piloting, and implementing), both authorized and unauthorized. This is expected to rise to 85% by 2026. By 2026, generative AI is anticipated to assist in more than 25% of software design, development, and testing work.

Challenges and Risks

Lack of Foundational Prerequisites

A major challenge facing organizations is the lack of foundational prerequisites for generative AI implementation. Only 27% of organizations have the platforms and tools in place, and 32% have the necessary talent prerequisites. Over 60% lack governance and upskilling programs for generative AI in software engineering.

Unofficial Usage Risks

Another significant risk stems from the informal use of generative AI tools. Of those who use generative AI, 63% employ unauthorized tools, exposing organizations to functional, security, and legal risks such as hallucinated code, code leakage, and intellectual property (IP) issues. Nearly a third of the workforce is self-training on generative AI, with less than 40% receiving formal training from their organizations.

Strategies to Realize the Full Potential

Select and Prioritize High-Benefit Use Cases

Organizations should identify and prioritize use cases that offer the highest benefits, such as coding assistance, test case generation, documentation, and code modernization. By focusing on these areas, organizations can maximize their investment in generative AI and realize quicker returns.

Mitigate Risks

A comprehensive risk management approach is crucial to mitigate security, IP/copyright, and code leakage risks.