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In today's landscape of rapidly evolving software engineering, managers face a multitude of challenges and complex decision-making scenarios. Within the Swedish Transport Administration (STA), software engineering managers take on dual roles as both strategists and traditional managers, increasing the complexity of their decision-making environment. We investigate how management teams in this context can use and adopt agile practices for better decision-making. The aim is to explore if agile software development team practices can be used for software engineering management teams, with the goal of identifying agile success factors that can be mapped to management success. We employ an industrial case study with a mixed-method research approach, combining quantitative agile data using project tracking software, and qualitative data using structured interviews with the management teams. Unlike previous research that has primarily examined agile adoption within software development teams or emphasized the manager's supportive role, this work investigates management teams themselves as adopters of agile practices and metrics. To our knowledge, this is the first study to develop a mapping model that systematically connects Scrum-based practices, roles, and metrics to the context of software engineering management teams. Our study showed that Scrum-based agile practices, such as stand-ups and retrospectives, can be adapted to software engineering management teams, and that certain agile software development metrics can be transformed into a managerial setting using our proposed model. © 2026 The Author(s). Journal of Software: Evolution and Process published by John Wiley & Sons Ltd.

Effective multi-attribute group decision-making (MAGDM) in complex scenarios often suffers from inherent challenges, which are unknown expert and attribute weights, and inherent information uncertainty. To overcome these limitations, this paper proposes a CRITIC-WASPAS method for solving MAGDM problems with unknown experts and attribute weights. The method integrates the Yager operator, expert weights, criteria importance through criteria correlation (CRITIC), and the weighted aggregate sum product assessment (WASPAS) decision-making method under interval-valued q-rung orthopair fuzzy sets (IVq-ROFS). Firstly, we extend the Yager weighted average operator (IVq-ROFYWA) and the Yager weighted geometric average operator (IVq-ROFYWG) under the IVq-ROFS framework. Secondly, we derive expert weights for different alternatives to avoid the deficiency of an overall decision-making perspective and attribute weights based on CRITIC under IVq-ROFS. Thirdly, the integrated CRITIC-WASPAS method based on the WASPAS method is introduced. Finally, two distinct domain cases are implemented to illustrate the effectiveness and broad applicability of the proposed CRITIC-WASPAS method. The implementation results across both domains are consistent with experts’ opinions, and the comparison and analysis results show that the CRITIC-WASPAS method is more effective and feasible.

Urban and transport planning can strongly affect energy usage induced by travel in cities. However, most studies investigate large cities with crude measurements of induced travel without consideration of the urban configuration of residences and their trip destinations, and little attention has been paid to smaller cities. We investigate energy usage (CO2-emissions) from car travel in a small Swedish city using a novel approach based on detailed GPS-tracking data of actual car mobility to calculate CO2-emissions on street segments and to identify major destinations. We also construct configuration scenarios, applied to the case city. These scenarios' induced CO2 emission from transports is evaluated in relation to the current configuration of the city. We find that changes in the urban configuration can impact on energy usage from intra-urban car travel by some 40% compared to the current situation and that the configurations display large relative differences in transport-efficiency, polycentric and public transport-based configurations being more efficient than monocentric development. We conclude that housing allocation is less important for car transport efficiency than re-location of existing destination points. Urban planning needs to be critical to over-simplified densification strategies and analyze the urban configuration to find optimal solutions.

In this paper, we address the challenges of random label ordering and limited interpretability associated with Ensemble Classifier Chains (ECC) by introducing a novel ECC method, ECC-MOO&BN, which integrates Bayesian Networks (BN) and Multi-Objective Optimization (MOO). This approach is designed to concurrently overcome these ECC limitations. The ECC-MOO&BN method focuses on extracting diverse and interpretable label orderings for the ECC classifier. We initiated this process by employing mutual information to investigate label relationships and establish the initial structures of the BN. Subsequently, an enhanced NSGA-II algorithm was applied to develop a series of Directed Acyclic Graphs (DAGs) that effectively balance the likelihood and complexity of the BN structure. The rationale behind using the MOO method lies in its ability to optimize both complexity and likelihood simultaneously, which not only diversifies DAG generation but also helps avoid overfitting during the production of label orderings. The DAGs, once sorted topologically, yielded a series of label orderings, which were then seamlessly integrated into the ECC framework for addressing multi-label classification (MLC) problems. Experimental results show that when benchmarked against eleven leading-edge MLC algorithms, our proposed method achieves the highest average ranking across seven evaluation criteria on nine out of thirteen MLC datasets. The results of the Friedman test and Nemenyi test also indicate that the performance of the proposed method has a significant advantage compared to other algorithms.

The ’black box’ characteristics of Convolutional Neural Networks (CNNs) present significant risks to their application scenarios, such as reliability, security, and division of responsibilities. Addressing the interpretability of CNN emerges as an urgent and critical issue in the field of machine learning. Recent research on CNN interpretability has either yielded unstable or inconsistent interpretations, or produced coarse-scale interpretable heatmaps, limiting their applicability in various scenarios. In this work, we propose a novel method of CNNs interpretation by incorporating a joint evaluation of multiple feature maps and employing multi-objective optimization (JE&MOO-CAM). Firstly, a method of joint evaluation for all feature maps is proposed to preserve the complete object instances and improve the overall activation values. Secondly, an interpretation method of CNNs under the MOO framework is proposed to avoid the instability and inconsistency of interpretation. Finally, the operators of selection, crossover, and mutation, along with the method of population initialization in NSGA-II, are redesigned to properly express the characteristics of CNNs. The experimental results, including both qualitative and quantitative assessments along with a sanity check conducted on three classic CNN models—VGG16, AlexNet, and ResNet50—demonstrate the superior performance of the proposed JE&MOO-CAM model. This model not only accurately pinpoints the instances within the image requiring explanation but also preserves the integrity of these instances to the greatest extent possible. These capabilities signify that JE&MOO-CAM surpasses six other leading state-of-the-art methods across four established evaluation criteria.

The concept of a Positive Energy District (PED) has become a vital component of the efforts to accelerate the transition to zero carbon emissions and climate-neutral living environments. Research is shifting its focus from energy-efficient single buildings to districts, where the aim is to achieve a positive energy balance across a given time period. Various innovation projects, programs, and activities have produced abundant insights into how to implement and operate PEDs. However, there is still no agreed way of determining what constitutes a PED for the purpose of identifying and evaluating its various elements. This paper thus sets out to create a process for characterizing PEDs. First, nineteen different elements of a PED were identified. Then, two AI techniques, machine learning (ML) and natural language processing (NLP), were introduced and examined to determine their potential for modeling, extracting, and mapping the elements of a PED. Lastly, state-of-the-art research papers were reviewed to identify any contribution they can make to the determination of the effectiveness of the ML and NLP models. The results suggest that both ML and NLP possess significant potential for modeling most of the identified elements in various areas, such as optimization, control, design, and stakeholder mapping. This potential is realized through the utilization of vast amounts of data, enabling these models to generate accurate and useful insights for PED planning and implementation. Several practical strategies have been identified to enhance the characterization of PEDs. These include a clear definition and quantification of the elements, the utilization of urban-scale energy modeling techniques, and the development of user-friendly interfaces capable of presenting model insights in an accessible manner. Thus, developing a holistic approach that integrates existing and novel techniques for PED characterization is essential to achieve sustainable and resilient urban environments.

Partial multi-label learning (PML) addresses problems where each instance is assigned a candidate label set and only a subset of these candidate labels is correct. The major challenge of PML is that the training procedure can be easily misguided by noisy labels. Current studies on PML have revealed two significant drawbacks. First, most of them do not sufficiently explore complex label correlations, which could improve the effectiveness of label disambiguation. Second, PML models heavily rely on prior assumptions, limiting their applicability to specific scenarios. In this work, we propose a novel method of PML based on the Encoder-Decoder Framework (PML-ED) to address the drawbacks. PML-ED initially achieves the distribution of label probability through a KNN label attention mechanism. It then adopts Conditional Layer Normalization (CLN) to extract the high-order label correlation and relaxes the prior assumption of label noise by introducing a universal Encoder-Decoder framework. This approach makes PML-ED not only more efficient compared to the state-of-the-art methods, but also capable of handling the data with large noisy labels across different domains. Experimental results on 28 benchmark datasets demonstrate that the proposed PML-ED model, when benchmarked against nine leading-edge PML algorithms, achieves the highest average ranking across five evaluation criteria.