The software industry has rapidly expanded, and software development is now essential to the success of many multinational corporations. The demand for complex software systems has also dramatically increased. Effective software development is essential, given the limitations of resources such as money, time, and labor. The cost and effort calculations affect the development process and client needs. Project failure was usually caused by errors in job estimating. Underestimating could have severe repercussions, if the project’s budget ends up being more than anticipated. Project overruns, on the other hand, can also have a detrimental impact on how successfully software projects are finished. Researchers and experts in the field of software are investigating ways to keep management and development productivity at high levels. However, a stand-alone estimation has not produced any noteworthy research results. Over the last decade, the standalone estimating models have revealed inadequacies. Literature suggests opting ensemble models, would yield better results as compared to standalone models. We have proposed a heterogeneous ensemble effort estimation (EEE) model in this research. The proposed model is comprises of standalone estimating models such as 1) Use Case Point, 2) Expert Judgment (EJ) and 3)Artificial neural network (ANN). Finally, using linear combination rules, the effort of each unique base model is combined. We applied our ensemble model on the benchmark dataset i.e. ISBSG using three different variations to avoid biasness. Furthermore, trained models were applied on industry use-cases for cross-validation. The findings of this study demonstrated that, in comparison to stand-alone estimate strategies, the ensemble technique gave better estimation results.

Software cost estimation is crucial to software management, which has received considerable attention from both industry and academia. Software size is an important metric that forms the cornerstone of software cost estimation. The function point has been proven to be a useful software size unit for size estimation and has been successfully implemented in many countries. However, in current practice, the rule of function point size method is complicated and performed manually. Consequently, it is costly in both time and resources spent to apply these methods, especially in the scenario of large-scale software development in the industry. In this paper, a deep learning-based named entity recognition (NER) model was designed in place of manual function point recognition. In particular, a BiLSTM-CRF model was trained on previously labeled requirements in the industry to classify the function point type of new requirements in the same domain. The proposed method was verified on 29 real projects provided by our industry partner. A comparative experiment was designed for the quantitative evaluation of efficiency improvement of the proposed NER model aided function point estimation. The result suggests that, for the NER model, the precision and F1 of the BiLSTM-CRF-based function point analysis on test samples achieved 94.5% and 80.3%, respectively. Moreover, the improvement in the efficiency of the software size estimation process achieved an average of 38.6%, which is a significant enhancement for the function point-based software size estimation.

With the accelerated digital transformation of the power industry, critical infrastructures such as power grids are increasingly migrating to cloud-native architectures, leading to unprecedented growth in service scale and complexity. Traditional operation and maintenance (O&M) methods struggle to meet the demands for real-time monitoring, accuracy, and scalability in such environments. This paper proposes a novel service performance anomaly detection system based on large language models (LLMs) and multi-agent systems (MAS). By integrating the semantic understanding capabilities of LLMs with the distributed collaboration advantages of MAS, we construct a high-precision and robust anomaly detection framework. The system adopts a hierarchical architecture, where lower-layer agents are responsible for tasks such as log parsing and metric monitoring, while an upper-layer coordinating agent performs multimodal feature fusion and global anomaly decision-making. Additionally, the LLM enhances the semantic analysis and causal reasoning capabilities for logs. Experiments conducted on real-world data from the State Grid Corporation of China, covering 1289 service combinations, demonstrate that our proposed system significantly outperforms traditional methods in terms of the F1-score across four platforms, including customer services and grid resources (achieving up to a 10.3% improvement). Notably, the system excels in composite anomaly detection and root cause analysis. This study provides an industrial-grade, scalable, and interpretable solution for intelligent power grid O&M, offering a valuable reference for the practical implementation of AIOps in critical infrastructures. Evaluated on real-world data from the State Grid Corporation of China (SGCC), our system achieves a maximum F1-score of 88.78%, with a precision of 92.16% and recall of 85.63%, outperforming five baseline methods.

Numerous factors have an impact on the temporal correction of integrated modular avionics application. As early as in the design phase, verification should be conducted to guarantee the rigorous temporal requirements for safety consideration which are fulfilled. This paper proposes a model for underlining verification in the design phase. The model captures the temporally related design in an industrial standard for software application and operation system (ARINC653). The finite-state machine mechanism is employed to formulate the behavior model of the application and application execution interface. Furthermore, temporal requirements are measured under the simulation of the state machine model. A case study is conducted on a real-world auto-pilot application that conformed with ARINC653.

With the expansion of microservice-based applications over time, the number of microservices rises, resulting in an augmentation of the volume of performance metrics. Consequently, selecting the appropriate performance metrics for anomaly detection becomes a critical challenge. Since these performance metrics are typically strongly correlated with timestamps, they form time series data comprising timestamp–value pairs. To address this, we propose SRdetector, a feature-enhanced Transformer-based model that adopts a time series forecasting approach to detect anomalies in microservices. Furthermore, we integrate a dynamic weight adjustment mechanism into the original Transformer attention mechanism to assign weights to different performance and temporal features. This enables the model to dynamically learn the significance of various features at different time intervals, effectively serving as a feature selection method for microservice performance metrics. Finally, anomaly detection in microservices is conducted by evaluating the predicted performance metric data based on confidence intervals.

The accurate forecasting of server performance metrics, such as CPU utilization, memory usage, and network bandwidth, is critical for optimizing resource allocation and ensuring system reliability in large-scale computing environments. In this paper, we introduce the Mixture of Experts for Decomposition Kolmogorov–Arnold Network (MOE-KAN), a novel approach designed to improve both the accuracy and interpretability of server performance prediction. The MOE-KAN framework employs a decomposition strategy that breaks down complex, nonlinear server performance patterns into simpler, more interpretable components, facilitating a clearer understanding of how predictions are made. By leveraging a Mixture of Experts (MOE) model, trend and residual components are learned by specialized experts, whose outputs are transparently combined to form the final prediction. The Kolmogorov–Arnold Network further enhances the model’s ability to capture intricate input–output relationships while maintaining transparency in its decision-making process. Experimental results on real-world server performance datasets demonstrate that MOE-KAN not only outperforms traditional models in terms of accuracy but also provides a more trustworthy and interpretable forecasting framework. This makes it particularly suitable for real-time server management and capacity planning, offering both reliability and interpretability in predictive models.