Robust geo-localization under changing environmental and operational conditions is critical for long-term aerial autonomy. Aerial visual place recognition (VPR) commonly uses pre-acquired remote-sensing imagery of the intended operating area, so the geographic label space can remain fixed while successive airborne missions introduce substantial visual distribution shifts. Continual adaptation to these shifts can cause catastrophic forgetting. We therefore formulate aerial VPR as a mission-based domain-incremental learning (DIL) problem and develop a heterogeneous memory framework. Before sequential adaptation, the satellite reference dataset is used once to train the initial model and construct a static satellite exemplar memory; a bounded replay buffer then retains selected airborne observations across missions. For replay management, we compare loss- and diversity-based selection criteria and introduce DBS-Hybrid, which combines prototype-based diversity trimming with representative-first feature-space coverage. Experiments on 21 visible and infrared UAV missions evaluate generalization to held-out missions, immediate adaptation, and knowledge retention. Under the primary Forward mission order, DBS-Hybrid achieves the highest mean final average accuracy, generalization, and knowledge retention among the evaluated methods, improving over the Random baseline by $5.06$, $5.32$, and $6.33$ percentage points, respectively, and improving backward transfer from -6.41% to 1.07%. Across five additional random mission orders, DBS-Hybrid ranks second in mean final average accuracy, backward transfer, generalization, and knowledge retention. Overall, heterogeneous memory and diversity-aware replay provide an effective basis for continual aerial VPR in mapped operating areas.
Agile - denoting "the quality of being agile, readiness for motion, nimbleness, activity, dexterity in motion" - software development methods are attempting to offer an answer to the eager business community asking for lighter weight along with faster and nimbler software development processes. This is especially the case with the rapidly growing and volatile Internet software industry as well as for the emerging mobile application environment. The new agile methods have evoked substantial amount of literature and debates. However, academic research on the subject is still scarce, as most of existing publications are written by practitioners or consultants. The aim of this publication is to begin filling this gap by systematically reviewing the existing literature on agile software development methodologies. This publication has three purposes. First, it proposes a definition and a classification of agile software development approaches. Second, it analyses ten software development methods that can be characterized as being "agile" against the defined criterion. Third, it compares these methods and highlights their similarities and differences. Based on this analysis, future research needs are identified and discussed.
P. Abrahamsson, O. Salo, Jussi Ronkainen et al.· arXiv.org· 728 citations· ⚡54
Context: Software startups are newly created companies with no operating history and fast in producing cutting-edge technologies. These companies develop software under highly uncertain conditions, tackling fast-growing markets under severe lack of resources. Therefore, software startups present a unique combination of characteristics which pose several challenges to software development activities. Objective: This study aims to structure and analyze the literature on software development in startup companies, determining thereby the potential for technology transfer and identifying software development work practices reported by practitioners and researchers. Method: We conducted a systematic mapping study, developing a classification schema, ranking the selected primary studies according their rigor and relevance, and analyzing reported software development work practices in startups. Results: A total of 43 primary studies were identified and mapped, synthesizing the available evidence on software development in startups. Only 16 studies are entirely dedicated to software development in startups, of which 10 result in a weak contribution (advice and implications (6); lesson learned (3); tool (1)). Nineteen studies focus on managerial and organizational factors. Moreover, only 9 studies exhibit high scientific rigor and relevance. From the reviewed primary studies, 213 software engineering work practices were extracted, categorized and analyzed. Conclusion: This mapping study provides the first systematic exploration of the state-of-art on software startup research. The existing body of knowledge is limited to a few high quality studies. Furthermore, the results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.
Nicolò Paternoster, Carmine Giardino, M. Unterkalmsteiner et al.· Information and Software Tec...· 394 citations· ⚡54
