the agile software development process has a set of standard agile ceremonies, which are must for an agile team to conduct in order to preserve its agility. However, there is always a challenge for agile teams to decide how much agile sprint time should be spent on hosting agile ceremonies and agile product build tasks. This is due to the fact that if most of the agile sprint time is spent on hosting agile ceremonies then the time for agile product build tasks will be reduced significantly impacting the velocity of the agile team. Hence, there is a need to find a solution with the help of which agile team can strike a right balance among various agile ceremonies and agile product build tasks during a sprint. To overcome this gap, this research paper analyses the data for 14 agile sprints to understand the agile time distribution across agile sprint ceremonies and agile product build tasks under current approach. Because of which, this research paper proposes the values for two newly introduced agile co-efficient namely coefficient of agile ceremony time and co-efficient of agile product build time to suggest the ideal time to be spent on agile ceremonies during a sprint. From the research results, it was evident that the velocity of the agile team following the proposed approach reported an increase in velocity by 13.96% for sprints when compared to the velocity of the agile team following the existing approach. The research work also highlighted that the duration chosen for agile ceremony by agile teams is independent of sprint lengths.

In the information age, people can access any information momentarily.With such power, internet providers and technology companies bear the responsibility for securing information as per the user's granted permissions.This paper will present a unified and stable pattern of privacy across all domains and present a stable model for privacy for unlimited applicability.The idea of this paper is to create stable functional and nonfunctional requirements and design the right to privacy to be used.While the functional requirements determine the purpose and the technical details of the system; nonfunctional requirements identify conceptual criteria of an effective system.We employ software stability model (SSM) versus the traditional model (business as usual) to define these requirements.We then employ a weighted study will then to compare the functional and non-functional requirements of privacy.The findings of this work are that (1) the Stable Privacy Model can be applied to all conceivable scenarios whereas the traditional model (TM) is limited to a single-use model that cannot be repurposed.Additionally, (2) the paper establishes the true functional and nonfunctional requirements of Privacy, and (3) creates a stable pattern language with unification, reusability, and unlimited applicability.

Every software development project is unique but still shares some similarities with other projects in terms of domain, database used, programming language employed, etc. Based upon which a lot of work has been done so far to predict project size, efforts required, budget required to build the software project using historical data. But, a very less attention has been given in modeling world to analyze the skills of agile team responsible for software project development. Hence, this paper suggests a simplified agile software project selection model using natural language processing based upon the agile team skills named as "Stair-case Model". After all it is the capability of agile team to deliver the project on time within budget. A strong skilled agile team having relevant experience has brighter chances to deliver the quality project on time and vice-versa.

Fayad's Knowledge Maps (KMs) form the basis, core and a strong foundation to understand any problem discipline and its solution patterns. KM forms the ability to create reusable and stable pattern languages and their analysis and design patterns. KMs' main idea is to allow practitioners and developers to master particular discipline of interest through true domain analysis using KMs, via stable patterns and an insightful methodological process. It gives practitioners and developers the necessary means and tools for a complete retrospective of the stable patterns that are pertinent to a discipline of special interest and the tidbits of advice, on how to use them in order to satisfy particular needs. Fayad's Knowledge Maps (KM) enable us to do a domain analysis of networking in a way this is not tied to a specific case or a specific condition, rather the domain analysis is done in a holistic and conceptual sense. The core knowledge related to networking is recorded, thus enabling comprehensive requirement analysis. The benefits do not stop there. KM's properties like intersection of different KMs, using remote KMs in association with the KM of any one domain under consideration lets us analyze the knowledge across various such Knowledge maps and thus multiple domains, as a whole and help us in coming up with requirements and design of any system that spans across more than one domains of applications. Any software that is architected and designed from the knowledge captured by Knowledge Maps, is done in a way that makes it highly adaptable in nature i.e. separation of concerns between functionality sets is extremely well done. Each part of the functionality can be taken out or some other can be plugged into the architecture, scaling it in whatever way it is required. A Knowledge Map lets us come up with a stable core software system rather quickly which consists of the core knowledge for one or more applications as and how any application context is brought into the system. With the ability to design a generic stable core that can be reused in multiple scenarios, enables it to be self-suited and self-adapted to a change in the application context. All of this is possible since it is built on the foundation of Fayad's Software Stability. This brings along a number of benefits, such as longevity, high returns on investments, self-configurability, Self-Adaptability, Self-Manageability, Easy customizability, unlimited reusability of the artifacts developed and much more.

Evidence is a very important concept that deals with augmenting the knowledge about a certain entity, be it any situation or artifact, so as to arrive to a proof for a given proposition. The Idea behind this work is to develop a Stable Model which helps in reusability of the pattern in any application applicable across multiple domains and scenarios without having to develop the infrastructure required to factor in any evidence in any system, again and again from scratch. The pattern captures the core knowledge related to whatever is required to develop a generic system that can help in factoring any evidence n to any system. The major applications of this Pattern are Judicial Law and major areas of Sciences scientific research. This pattern can be used as standalone system or it can be used as part of other pattern. The pattern is stable, i.e. it does not change over time even if the environment in which the system has to operate changes and thus it ensures that it indeed meets the requirements that it was supposed to meet over time, without having to go through a lot of maintenance when it has to be applied is some other application context than what was originally intended.

The challenge of building efficient reusable software artifacts is the focus of several schools of thought in software engineering. Software analysis patterns are recurring and reusable models. However, there are several deficiencies with analysis patterns. These deficiencies make it difficult to use analysis patterns as efficient reusable artifacts. This paper proposes eight essential properties to evaluate pattern reusability. In addition, the concept of Stability Analysis Patterns is introduced. This paper contrasts stable analysis patterns with some analysis patterns using the proposed properties.

The rapid growth of Intelligent Systems, the challenges and technology, coupled with the tightened intelligent software developments time and production, and cost constraints has imposed tremendous pressure on software industry to create new and innovative designs, which respond to rapidly changing business and operating environments. Software industry must invest in building stable architectures that are flexible and can be easily adapted. We refer to these emerging trends of architectures as Unified Software Architectures on Demand (USA on-Demand): which is based on Software Stability Model and Knowledge Maps as they can be self- adaptive, easily-customizable, self-extensible, more personalizable, self-configurable, and self-manageable accordingly to meet the future requirements and changes in the operating environments. USA on-Demand presents two complete intelligent systems case studies and much more will be discussed during the conference. A good architecture provides the design principles to ensure, a roadmap for that portion of the road which is yet to be built. Self-configurable and self-manageable architectures, refer to architectures that can manage and “self-heal” its properties vigorously during the reconfiguring runtime of the components, connectors, and the underlying infrastructure.