Recent progress in large language models (LLMs) has advanced automatic code generation, yet most approaches rely on direct, single-step translation from problem descriptions to code, disregarding structured software engineering practices. We introduce a lifecycle-aware framework that systematically incorporates intermediate artifacts such as requirements analysis, state machine modeling, and pseudocode into both the training and inference stages. This design aligns code generation with standard software development phases and enables more structured reasoning. Experiments show that lifecycle-level fine-tuning improves code correctness by up to 75% over the same model before fine-tuning, with performance gains compounding across intermediate stages. Multi-step inference consistently surpasses single-step generation, demonstrating the effectiveness of intermediate scaffolding. Notably, open-source LLMs, once fine-tuned under our framework, match or slightly outperform models pretrained on code. When applied to DeepSeek-Coder-1.3B, our framework yields relative CodeBLEU improvements of 34.3%, 20.0%, 11.2%, and 22.3% over ChatGPT-3.5, ChatGPT-4o-mini, DeepSeek-R1, and LLaMA-8B, respectively. Our pipeline also proves robust with up to 80\% less training data, confirming its resilience. Ablation studies further reveal that each intermediate artifact contributes distinctly to final code quality, with state machine modeling yielding the most substantial impact. Our source code and detailed experimental data are available at https://anonymous.4open.science/r/Lifecycle-Aware-3CCB.

Artificial intelligence (AI) permeates all fields of life, which resulted in new challenges in requirements engineering for artificial intelligence (RE4AI), e.g., the difficulty in specifying and validating requirements for AI or considering new quality requirements due to emerging ethical implications. It is currently unclear if existing RE methods are sufficient or if new ones are needed to address these challenges. Therefore, our goal is to provide a comprehensive overview of RE4AI to researchers and practitioners. What has been achieved so far, i.e., what practices are available, and what research gaps and challenges still need to be addressed? To achieve this, we conducted a systematic mapping study combining query string search and extensive snowballing. The extracted data was aggregated, and results were synthesized using thematic analysis. Our selection process led to the inclusion of 126 primary studies. Existing RE4AI research focuses mainly on requirements analysis and elicitation, with most practices applied in these areas. Furthermore, we identified requirements specification, explainability, and the gap between machine learning engineers and end-users as the most prevalent challenges, along with a few others. Additionally, we proposed seven potential research directions to address these challenges. Practitioners can use our results to identify and select suitable RE methods for working on their AI-based systems, while researchers can build on the identified gaps and research directions to push the field forward.

Several approaches have been presented, which aim to extract models from natural language specifications. These approaches have inherent weaknesses for they assume an initial problem understanding that is perfect, and they leave no room for feedback. Motivated by real-world collaboration settings between requirements engineers and customers, this paper proposes an interaction blueprint that aims for dialogue based, computer aided software requirements analysis. Compared to mere model extraction approaches, this interaction blueprint encourages individuality, creativity and genuine compromise. A simplistic Experiment was conducted to showcase the general idea. This paper discusses the experiment as well as the proposed interaction blueprint and argues, that advancements in natural language processing and generative AI might lead to significant progress in a foreseeable future. However, for that, there is a need to move away from a magical black box expectation and instead moving towards a dialogue based approach that recognizes the individuality that is an undeniable part of requirements engineering.

Since the release of the ChatGPT interactive AI assistant it has been surprising to see some of the snide, passive-aggressive reactions from some (not all) members of the software engineering community, in the style of "it's just inference from bad data". Let's get real, folks, it is truly game-changing. Basically, if you need a program element and can describe that need, the assistant will generate it for you. There is no particular restriction on the programming language that you choose, as long as its description and enough examples are available somewhere. The code will be pretty good.

A Bachelor's Degree in Computer Science or in a related technical field is highly desired which will be considered equivalent to two (2) years of experience A Master's degree in a Technical Field will be considered equivalent to four (4) years of experience A degree in Mathematics, Information Systems, Engineering, or similar degree will be considered as a technical field Eight (8) years of experience in software development/engineering, including requirements analysis, software development, installation, integration, evaluation, enhancement, maintenance, testing, and problem diagnosis/resolution and at least six (6) years of experience developing software with high level languages such as Java, C, C Demonstrated ability to work with OpenSource (NoSQL) products that support highly distributed, massively parallel computation needs such as Hbase, Acumulo, Big Table, etcAd: Ready to find your dream job? Use this free career assessment test to figure it out. Peraton drives missions of consequence spanning the globe and extending to the farthest reaches of the galaxy As the world's leading mission capability integrator and transformative enterprise IT provider, we deliver trusted and highly differentiated national security solutions and technologies that keep people safe and secureAd: Stop spending hours editing your resume to fit job descriptions. Peraton serves as a valued partner to essential government agencies across the intelligence, space, cyber, defense, civilian, health, and state and local markets Every day, our employees do the can't be done, solving the most daunting challenges facing our customers For Colorado Residents: Colorado Salary Minimum: $90,500 Colorado Salary Maximum: $219,700 The estimate displayed represents the typical salary range for this position, and is just one component of Peraton's total compensation package for employees Other rewards may include annual bonuses, short- and long-term incentives, and program-specific awards In addition, Peraton provides a variety of benefits to employees

Publicly available datasets are one of the key drivers for commercial AI software. The use of publicly available datasets (particularly for commercial purposes) is governed by dataset licenses. These dataset licenses outline the rights one is entitled to on a given dataset and the obligations that one must fulfil to enjoy such rights without any license compliance violations. However, unlike standardized Open Source Software (OSS) licenses, existing dataset licenses are defined in an ad-hoc manner and do not clearly outline the rights and obligations associated with their usage. This makes checking for potential license compliance violations difficult. Further, a public dataset may be hosted in multiple locations and created from multiple data sources each of which may have different licenses. Hence, existing approaches on checking OSS license compliance cannot be used. In this paper, we propose a new approach to assess the potential license compliance violations if a given publicly available dataset were to be used for building commercial AI software. We conduct trials of our approach on two product groups within Huawei on 6 commonly used publicly available datasets. Our results show that there are risks of license violations on 5 of these 6 studied datasets if they were used for commercial purposes. Consequently, we provide recommendations for AI engineers on how to better assess publicly available datasets for license compliance violations.

There seems no end to the plethora of software solutions suddenly seeming to have acquired the quality of artificial intelligence (AI). Little more than a decade after phones reportedly grew "smart," you might now be wondering whether technology had crossed yet another historic threshold. For those of us who grew up watching 2001 A Space Odyssey and Knight Rider, the concept of non-human intelligence--whether benevolent or malevolent--is nothing new. Not only does science fiction fuel our expectations, it has often demonstrated an uncanny ability at predicting real life technological advancements. Is the age of artificial intelligence now upon us?

At 62 % CAGR, Artificial Intelligence Market Potentially Worth $16.06 Billion by 2022 Led by North ... At 62 % CAGR, Artificial Intelligence Market Potentially Worth $16.06 Billion by 2022 Led by North ... New Amazon AI services will bring Alexa's smarts, and a lot more, to your mobile app Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time.