Influential factors of aligning Spotify squads in mission-critical and offshore projects – a longitudinal embedded case study

Changing the development process of an organization is one of the toughest and riskiest decisions. This is particularly true if the known experiences and practices of the new considered ways of working are relative and subject to contextual assumptions. Spotify engineering culture is deemed as a new agile software development method which increasingly attracts large-scale organizations. The method relies on several small cross-functional self-organized teams (i.e., squads). The squad autonomy is a key driver in Spotify method, where a squad decides what to do and how to do it. To enable effective squad autonomy, each squad shall be aligned with a mission, strategy, short-term goals and other squads. Since a little known about Spotify method, there is a need to answer the question of: How can organizations work out and maintain the alignment to enable loosely coupled and tightly aligned squads? In this paper, we identify factors to support the alignment that is actually performed in practice but have never been discussed before in terms of Spotify method. We also present Spotify Tailoring by highlighting the modified and newly introduced processes to the method. Our work is based on a longitudinal embedded case study which was conducted in a real-world large-scale offshore software intensive organization that maintains mission-critical systems. According to the confidentiality agreement by the organization in question, we are not allowed to reveal a detailed description of the features of the explored project

An empirical cognitive model of the development of shared understanding of requirements

It is well documented that customers and software development teams need to share and refine understanding of the requirements throughout the software development lifecycle. The development of this shared understand- ing is complex and error-prone however. Techniques and tools to support the development of a shared understanding of requirements (SUR) should be based on a clear conceptualization of the phenomenon, with a basis on relevant theory and analysis of observed practice. This study contributes to this with a detailed conceptualization of SUR development as sequence of group-level state transi- tions based on specializing the Team Mental Model construct. Furthermore it proposes a novel group-level cognitive model as the main result of an analysis of data collected from the observation of an Agile software development team over a period of several months. The initial high-level application of the model shows it has promise for providing new insights into supporting SUR development

Degrees of tenant isolation for cloud-hosted software services : a cross-case analysis

A challenge, when implementing multi-tenancy in a cloud-hosted software service, is how to ensure that the performance and resource consumption of one tenant does not adversely affect other tenants. Software designers and architects must achieve an optimal degree of tenant isolation for their chosen application requirements. The objective of this research is to reveal the trade-offs, commonalities, and differences to be considered when implementing the required degree of tenant isolation. This research uses a cross-case analysis of selected open source cloud-hosted software engineering tools to empirically evaluate varying degrees of isolation between tenants. Our research reveals five commonalities across the case studies: disk space reduction, use of locking, low cloud resource consumption, customization and use of plug-in architecture, and choice of multi-tenancy pattern. Two of these common factors compromise tenant isolation. The degree of isolation is reduced when there is no strategy to reduce disk space and customization and plug-in architecture is not adopted. In contrast, the degree of isolation improves when careful consideration is given to how to handle a high workload, locking of data and processes is used to prevent clashes between multiple tenants and selection of appropriate multi-tenancy pattern. The research also revealed five case study differences: size of generated data, cloud resource consumption, sensitivity to workload changes, the effect of the software process, client latency and bandwidth, and type of software process. The degree of isolation is impaired, in our results, by the large size of generated data, high resource consumption by certain software processes, high or fluctuating workload, low client latency, and bandwidth when transferring multiple files between repositories. Additionally, this research provides a novel explanatory framework for (i) mapping tenant isolation to different software development processes, cloud resources and layers of the cloud stack; and (ii) explaining the different trade-offs to consider affecting tenant isolation (i.e. resource sharing, the number of users/requests, customizability, the size of generated data, the scope of control of the cloud application stack and business constraints) when implementing multi-tenant cloud-hosted software services. This research suggests that software architects have to pay attention to the trade-offs, commonalities, and differences we identify to achieve their degree of tenant isolation requirements

A cross-sector analysis of human and organisational factors in the deployment of data-driven predictive maintenance

Domains such as utilities, power generation, manufacturing and transport are increasingly turning to data-driven tools for management and maintenance of key assets. Whole ecosystems of sensors and analytical tools can provide complex, predictive views of network asset performance. Much research in this area has looked at the technology to provide both sensing and analysis tools. The reality in the field, however, is that the deployment of these technologies can be problematic due to user issues, such as interpretation of data or embedding within processes, and organisational issues, such as business change to gain value from asset analysis. 13 experts from the field of remote condition monitoring, asset management and predictive analytics across multiple sectors were interviewed to ascertain their experience of supplying data-driven applications. The results of these interviews are summarised as a framework based on a predictive maintenance project lifecycle covering project motivations and conception, design and development, and operation. These results identified critical themes for success around having a target or decision-led, rather than data-led, approach to design; long-term resourcing of the deployment; the complexity of supply chains to provide data-driven solutions and the need to maintain knowledge across the supply chain; the importance of fostering technical competency in end-user organisations; and the importance of a maintenance-driven strategy in the deployment of data-driven asset management. Emerging from these themes are recommendations related to culture, delivery process, resourcing, supply chain collaboration and industry-wide cooperation

REI:An integrated measure for software reusability

To capitalize upon the benefits of software reuse, an efficient selection among candidate reusable assets should be performed in terms of functional fitness and adaptability. The reusability of assets is usually measured through reusability indices. However, these do not capture all facets of reusability, such as structural characteristics, external quality attributes, and documentation. In this paper, we propose a reusability index (REI) as a synthesis of various software metrics and evaluate its ability to quantify reuse, based on IEEE Standard on Software Metrics Validity. The proposed index is compared with existing ones through a case study on 80 reusable open-source assets. To illustrate the applicability of the proposed index, we performed a pilot study, where real-world reuse decisions have been compared with decisions imposed by the use of metrics (including REI). The results of the study suggest that the proposed index presents the highest predictive and discriminative power; it is the most consistent in ranking reusable assets and the most strongly correlated to their levels of reuse. The findings of the paper are discussed to understand the most important aspects in reusability assessment (interpretation of results), and interesting implications for research and practice are provided

Considering Polymorphism in Change-Based Test Suite Reduction

With the increasing popularity of continuous integration, algorithms for selecting the minimal test-suite to cover a given set of changes are in order. This paper reports on how polymorphism can handle false negatives in a previous algorithm which uses method-level changes in the base-code to deduce which tests need to be rerun. We compare the approach with and without polymorphism on two distinct cases ---PMD and CruiseControl--- and discovered an interesting trade-off: incorporating polymorphism results in more relevant tests to be included in the test suite (hence improves accuracy), however comes at the cost of a larger test suite (hence increases the time to run the minimal test-suite).Comment: The final publication is available at link.springer.co

Data Pipeline Management in Practice: Challenges and Opportunities

Data pipelines involve a complex chain of interconnected activities that starts with a data source and ends in a data sink. Data pipelines are important for data-driven organizations since a data pipeline can process data in multiple formats from distributed data sources with minimal human intervention, accelerate data life cycle activities, and enhance productivity in data-driven enterprises. However, there are challenges and opportunities in implementing data pipelines but practical industry experiences are seldom reported. The findings of this study are derived by conducting a qualitative multiple-case study and interviews with the representatives of three companies. The challenges include data quality issues, infrastructure maintenance problems, and organizational barriers. On the other hand, data pipelines are implemented to enable traceability, fault-tolerance, and reduce human errors through maximizing automation thereby producing high-quality data. Based on multiple-case study research with five use cases from three case companies, this paper identifies the key challenges and benefits associated with the implementation and use of data pipelines

Совершенствование системы электроснабжения ОАО "Гомельский химический завод" в связи с разработкой мероприятий по экономии электрической энергии

Background: High suicide intent, childhood trauma, and violent behavior are risk factors for suicide in suicide attempters. The aim of this study was to investigate whether the combined assessment of suicide intent and interpersonal violence would provide a better prediction of suicide risk than an assessment of only suicide intent or interpersonal violence. Methods: This is a cohort study involving 81 suicide attempters included in the study between 1993 and 1998. Patients were assessed with both the Suicide Intent Scale (SIS) and the Karolinska Interpersonal Violence Scale (KIVS). Through the unique personal identification number in Sweden, patients were linked to the Cause of Death Register maintained by the Swedish National Board of Health and Welfare. Suicides were ascertained from the death certificates. Results: Seven of 14 patients who had died before April 2013 had committed suicide. The positive predictive value for the Suicide Intent Scale alone was 16.7 %, with a specificity of 52 % and an area under the curve of 0.74. A combined assessment with the KIVS gave higher specificity (63 %) and a positive predictive value of 18.8 % with an AUC of 0.83. Combined use of SIS and KIVS expressed interpersonal violence as an adult subscale gave a sensitivity of 83.3 %, a specificity of 80.3 %, and a positive predictive value of 26 % with an AUC of 0.85. The correlation between KIVS and SIS scores was not significant. Conclusions: Using both the the SIS and the KIVS combined may be better for predicting completed suicide than using them separately. The nonsignificant correlation between the scales indicates that they measure different components of suicide risk

Familial clustering of suicide risk: a total population study of 11.4 million individuals

BACKGROUND: Research suggests that suicidal behaviour is aggregated in families. However, due to methodological limitations, including small sample sizes, the strength and pattern of this aggregation remains uncertain.MethodWe examined the familial clustering of completed suicide in a Swedish total population sample. We linked the Cause of Death and Multi-Generation Registers and compared suicide rates among relatives of all 83 951 suicide decedents from 1952-2003 with those among relatives of population controls. RESULTS: Patterns of familial aggregation of suicide among relatives to suicide decedents suggested genetic influences on suicide risk; the risk among full siblings (odds ratio 3.1, 95% confidence interval 2.8-3.5, 50% genetic similarity) was higher than that for maternal half-siblings (1.7, 1.1-2.7, 25% genetic similarity), despite similar environmental exposure. Further, monozygotic twins (100% genetic similarity) had a higher risk than dizygotic twins (50% genetic similarity) and cousins (12.5% genetic similarity) had higher suicide risk than controls. Shared (familial) environmental influences were also indicated; siblings to suicide decedents had a higher risk than offspring (both 50% genetically identical but siblings having a more shared environment, 3.1, 2.8-3.5 v. 2.0, 1.9-2.2), and maternal half-siblings had a higher risk than paternal half-siblings (both 50% genetically identical but the former with a more shared environment). Although comparisons of twins and half-siblings had overlapping confidence intervals, they were supported by sensitivity analyses, also including suicide attempts. CONCLUSIONS: Familial clustering of suicide is primarily influenced by genetic and also shared environmental factors. The family history of suicide should be considered when assessing suicide risk in clinical settings or designing and administering preventive interventions