Analysing the Obstacles in Agile Software Development Approach: A Review

The development of reusable and extensible software for business purposes has been the hallmark of the day. More developers are taking advantage of numerous approaches towards reaching their goals. One such approach is the agile approach in the development of extensible applications which has become so popular since its introduction over a decade ago. Using an agile approach that has a defined value in developing applications portray numerous benefits which have been identified by various scholars pointing out their outcomes as motivating factors of its adoption. With all such outline benefits, there exist some potential obstacles to agile developmental approach which has not been fully addressed. Hence, this article is aimed at analysing the obstacles which software developers face during agile development through a database search and also to guide them on ways to overcome such obstacles.

scvi-tools: a library for deep probabilistic analysis of single-cell omics data

Probabilistic models have provided the underpinnings for state-of-the-art performance in many single-cell omics data analysis tasks, including dimensionality reduction, clustering, differential expression, annotation, removal of unwanted variation, and integration across modalities. Many of the models being deployed are amenable to scalable stochastic inference techniques, and accordingly they are able to process single-cell datasets of realistic and growing sizes. However, the community-wide adoption of probabilistic approaches is hindered by a fractured software ecosystem resulting in an array of packages with distinct, and often complex interfaces. To address this issue, we developed scvi-tools (https://scvi-tools.org), a Python package that implements a variety of leading probabilistic methods. These methods, which cover many fundamental analysis tasks, are accessible through a standardized, easy-to-use interface with direct links to Scanpy, Seurat, and Bioconductor workflows. By standardizing the implementations, we were able to develop and reuse novel functionalities across different models, such as support for complex study designs through nonlinear removal of unwanted variation due to multiple covariates and reference-query integration via scArches. The extensible software building blocks that underlie scvi-tools also enable a developer environment in which new probabilistic models for single cell omics can be efficiently developed, benchmarked, and deployed. We demonstrate this through a code-efficient reimplementation of Stereoscope for deconvolution of spatial transcriptomics profiles. By catering to both the end user and developer audiences, we expect scvi-tools to become an essential software dependency and serve to formulate a community standard for probabilistic modeling of single cell omics.

hIPPYlib

We present an extensible software framework, hIPPYlib, for solution of large-scale deterministic and Bayesian inverse problems governed by partial differential equations (PDEs) with (possibly) infinite-dimensional parameter fields (which are high-dimensional after discretization). hIPPYlib overcomes the prohibitively expensive nature of Bayesian inversion for this class of problems by implementing state-of-the-art scalable algorithms for PDE-based inverse problems that exploit the structure of the underlying operators, notably the Hessian of the log-posterior. The key property of the algorithms implemented in hIPPYlib is that the solution of the inverse problem is computed at a cost, measured in linearized forward PDE solves, that is independent of the parameter dimension. The mean of the posterior is approximated by the MAP point, which is found by minimizing the negative log-posterior with an inexact matrix-free Newton-CG method. The posterior covariance is approximated by the inverse of the Hessian of the negative log posterior evaluated at the MAP point. The construction of the posterior covariance is made tractable by invoking a low-rank approximation of the Hessian of the log-likelihood. Scalable tools for sample generation are also discussed. hIPPYlib makes all of these advanced algorithms easily accessible to domain scientists and provides an environment that expedites the development of new algorithms.

Directions for future research on the integration of SOA, BPM, and BRM

Purpose Digital transformation has been changing the economic environment of enterprises in recent years. To remain competitive, they rely on an extensible software architecture, flexible workflow execution, and automated decision management. The concepts of service-oriented architectures (SOA), business process management (BPM), and business rules management (BRM) provide an adequate, yet isolated means of addressing many of these requirements. The paper aims to discuss these issues. Design/methodology/approach This study adapts from established research frameworks to structure the current state of research on the integration of SOA, BPM and BRM. The authors highlight the current research scope, point to disregarded topics and sketch out multidisciplinary research approaches. Findings While the three concepts are often discussed only in isolation or pairwise, approaches that integrate them are scarce. Against this backdrop, this study defines three types of research opportunities and identifies several directions for future research that should be explored from a technological as well as organizational perspective. Given the breadth of open questions, the authors present sources for each area of our research framework, which can serve as starting points for future investigations. Practical implications Except for well-established support for separate tasks and technologies, there is a lack of integrated standards, methods and platforms, which for example enable integrated decision-making across BPM and SOA. Originality/value Our contribution builds on established frameworks and clearly shows that the discussion on the integration of SOA, BPM and BRM cannot yet be regarded as sufficient. The research agenda highlights which areas explicitly benefit from a more precise consideration.