Will Tracz, our esteemed editor and Used-Program salesman, has written an entertaining, non-technical book dealing with the practice (and lack of) of software reuse. Its a collection of essays, mostly rehashed (reused?) and updated from various columns and papers published over the years.. Its a short (a bit over 200 pages) easy reading and enjoyable book (I read most of it in one sitting). Some of the essays discuss what was printed in the past and a discussion of the current status of the points.

Safeware: System Safety and Computers

Software architectures have high costs for change, are complex, and erode during evolution We believe these problems are partially due to knowledge vaporization Currently, almost all the knowledge and information about the design decisions the architecture is based on are implicitly embedded in the architecture, but lack a first-class representation Consequently, knowledge about these design decisions disappears into the architecture, which leads to the aforementioned problems In this paper, a new perspective on software architecture is presented, which views software architecture as a composition of a set of explicit design decisions This perspective makes architectural design decisions an explicit part of a software architecture Consequently, knowledge vaporization is reduced, thereby alleviating some of the fundamental problems of software architecture

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Software Architecture as a Set of Architectural Design Decisions

http://courses.ece.ubc.ca/417/public/Hofmeister.pdf

A general model of software architecture design derived from five industrial approaches

Models can help software engineers to reason about design-time decisions before implementing a system. This paper focuses on models that deal with non-functional properties, such as reliability and performance. To build such models, one must rely on numerical estimates of various parameters provided by domain experts or extracted by other similar systems. Unfortunately, estimates are seldom correct. In addition, in dynamic environments, the value of parameters may change over time. We discuss an approach that addresses these issues by keeping models alive at run time and feeding a Bayesian estimator with data collected from the running system, which produces updated parameters. The updated model provides an increasingly better representation of the system. By analyzing the updated model at run time, it is possible to detect or predict if a desired property is, or will be, violated by the running implementation. Requirement violations may trigger automatic reconfigurations or recovery actions aimed at guaranteeing the desired goals. We illustrate a working framework supporting our methodology and apply it to an example in which a Web service orchestrated composition is modeled through a Discrete Time Markov Chain. Numerical simulations show the effectiveness of the approach.

Model evolution by run-time parameter adaptation

This paper summarizes the current state of the art and recent trends in software engineering economics. It provides an overview of economic analysis techniques and their applicability to software engineering and management. It surveys the field of software cost estimation, including the major estimation techniques available, the state of the art in algorithmic cost models, and the outstanding research issues in software cost estimation.

Software engineering economics

The disciplines of requirements engineering (RE) and software architecture (SA) are fundamental to the success of software projects. Even though RE and SA are often considered in isolation, drawing a line between RE and SA is neither feasible nor reasonable as requirements and architectural design impact each other. This observation motivated the Twin Peaks model that was the subject of the Second International Workshop on the Twin Peaks of Requirements and Architecture (TwinPeaks@ICSE 2013). TwinPeaks@ICSE 2013 was held in conjunction with the 35th International Conference on Software Engineering 2013 in San Francisco, CA. The workshop aimed at providing a forum for researchers, practitioners and educators from the areas of RE and SA to discuss their experiences, forge new collaborations, and explore innovative solutions that address the challenges that occur when relating RE and SA. The workshop provided participants with an opportunity to become familiar with the relationship between RE and SA in the broader context of software engineering, rather than in an isolated context of either RE or SA. The workshop featured one industrial keynote, five research paper presentations, two invited talks and four working group discussions.

/pdf/views-on-software-engineering-from-the-twin-peaks-of-2x4dkhs9rx.pdf

Views on software engineering from the twin peaks of requirements and architecture

Detection of COVID-19 in X-Ray Images by Classification of Bag of Visual Words Using Neural Networks.

Given the advanced set of capabilities offered by smartphone and tablet computing devices, they have become the platform of choice for many users for day-to-day work and leisure. There is however a fundamental difference in the attitude of a typical user when it comes to using their mobile device as compared to their personal computers. While the use of anti-virus software on PCs to protect our security and privacy is ubiquitous today, there is little by the way of security and privacy protection available on these mobile computing platforms. Our work focuses on developing a Layered Intrusion Detection and Remediation framework (LIDAR) to automatically detect, analyze, protect, and remediate security threats in this domain. We have focused on Android devices and have developed several algorithms that would help detect abnormal behavior in the operation of Android smartphone and tablets that could potentially detect presence of malware. In this paper, we present a high-level overview of our approach and briefly summarize a suite of algorithms developed to identify certain types of malicious behavior.

LIDAR: a layered intrusion detection and remediationframework for smartphones

The new coronavirus infection has shocked the world since early 2020 with its aggressive outbreak. Rapid detection of the disease saves lives, and relying on medical imaging (Computed Tomography and X-ray) to detect infected lungs has shown to be effective. Deep learning and convolutional neural networks have been used for image analysis in this context. However, accurate identification of infected regions has proven challenging for two main reasons. Firstly, the characteristics of infected areas differ in different images. Secondly, insufficient training data makes it challenging to train various machine learning algorithms, including deep-learning models. This paper proposes an approach to segment lung regions infected by COVID-19 to help cardiologists diagnose the disease more accurately, faster, and more manageable. We propose a bifurcated 2-D model for two types of segmentation. This model uses a shared encoder and a bifurcated connection to two separate decoders. One decoder is for segmentation of the healthy region of the lungs, while the other is for the segmentation of the infected regions. Experiments on publically available images show that the bifurcated structure segments infected regions of the lungs better than state of the art.

Roshanak Roshandel

Papers

Views on software engineering from the twin peaks of requirements and architecture

Detection of COVID-19 in X-Ray Images by Classification of Bag of Visual Words Using Neural Networks.

LIDAR: a layered intrusion detection and remediationframework for smartphones

Bifurcated autoencoder for segmentation of covid-19 infected regions in ct images

Classification of diabetic retinopathy using unlabeled data and knowledge distillation.