Summary A natural disaster is an inevitable situation that can occur at any time and anywhere along with varied forms such as earthquakes, floods, hurricane, wildfire, and so forth, and different level of occurrences has been recorded from mild to an intense level. Timely disaster response plays an important role in reducing its deteriorate after‐effects and can save countless lives. Over the years, people have been developing guidelines and processes to cope up with such kinds of hazardous situations. In recent years, with the advent of the pervasive computing paradigm, situation‐awareness has been considered to be the most fascinating approach for situation assessment and provides decision support accordingly. Situation‐aware systems observe/perceive dynamic changes in the environment, understand/comprehend the situation, and perform actions according to the environment. Although state‐of‐the‐art formalisms have been developed to handle such kinds of hazardous situations intelligently and rescue the victims. However, belief‐desire‐intention (BDI) reasoning mechanism with the incorporation of situation‐awareness is still the thirsty area of research to manage hazardous situations. In this article, we propose a temporal epistemic situation‐aware formalism for BDI reasoning agents to model the context‐aware decision support system. To demonstrate the work of the proposed formalism, we develop a case study based on a disaster situation, in which BDI agents are modeled and simulated to present the results in terms of agents' reasoning processes. We demonstrate the scalability in temporal aspects of the system using different levels of disasters to monitor the hazardous situations and evaluate the overall behavior of the system.

Abstract Software effort estimation accuracy is a key factor in effective planning, controlling, and delivering a successful software project within budget and schedule. The overestimation and underestimation both are the key challenges for future software development, henceforth there is a continuous need for accuracy in software effort estimation. The researchers and practitioners are striving to identify which machine learning estimation technique gives more accurate results based on evaluation measures, datasets and other relevant attributes. The authors of related research are generally not aware of previously published results of machine learning effort estimation techniques. The main aim of this study is to assist the researchers to know which machine learning technique yields the promising effort estimation accuracy prediction in software development. In this article, the performance of the machine learning ensemble and solo techniques are investigated on publicly and non‐publicly domain datasets based on the two most commonly used accuracy evaluation metrics. We used the systematic literature review methodology proposed by Kitchenham and Charters. This includes searching for the most relevant papers, applying quality assessment (QA) criteria, extracting data, and drawing results. We have evaluated a state‐of‐the‐art accuracy performance of 35 selected studies (17 ensemble, 18 solo) using mean magnitude of relative error and PRED (25) as a set of reliable accuracy metrics for performance evaluation of accuracy among two techniques to report the research questions stated in this study. We found that machine learning techniques are the most frequently implemented in the construction of ensemble effort estimation (EEE) techniques. The results of this study revealed that the EEE techniques usually yield a promising estimation accuracy than the solo techniques.

Predelivery maintenance is one of the processes that has not received enough attention both within the academia and industry. Still, it counts as one of the least visible lifecycle processes. In this paper, we make it more visible by demarcating its scope within software lifecycle. We do it within eleven companies. Our goal is to find out how the industry understands predelivery maintenance and how it places it within software lifecycle. Our results show that predelivery maintenance is conducted by all the organizations studied in five different contexts. These organizations however use different terms for it or they treat predelivery maintenance as part of other processes. For this reason, we conclude that predelivery maintenance is a process that cross-cuts several lifecycle processes.

Despite the fact that a handover process is just as frequently performed as any development process, little is known about it. Still, it is regarded as one of the lifecycle processes that is not well explored and defined. In this paper, we study the handover process within eighteen companies with the purpose of demarcating its scope within software lifecycle. Our goal is to find out how industry understands handover process and how it places it within software lifecycle. As a result, we have identified seven different scope contexts for the handover process. We have also provided evidence of its wide lifecycle span and its overlap with development, predelivery and postdelivery maintenance processes.