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The Latest Research in Software Engineering and Cybersecurity

This post is the first in a two-part series highlighting 10 recommended practices for achieving agile at scale.

Software and acquisition professionals often have questions about recommended practices related to modern software development methods, techniques, and tools, such as how to apply agile methods in government acquisition frameworks, systematic verification and validation of safety-critical systems, and operational risk management. In the Department of Defense (DoD), these techniques are just a few of the options available to face the myriad challenges in producing large, secure software-reliant systems on schedule and within budget.

In their haste to deliver software capabilities, developers sometimes engage in less-than-optimal coding practices. If not addressed, these shortcuts can ultimately yield unexpected rework costs that offset the benefits of rapid delivery. Technical debt conceptualizes the tradeoff between the short-term benefits of rapid delivery and long-term value. Taking shortcuts to expedite the delivery of features in the short term incurs technical debt, analogous to financial debt, that must be paid off later to optimize long-term success. Managing technical debt is an increasingly critical aspect of producing cost-effective, timely, and high-quality software products, especially in projects that apply agile methods.

In their current state, wearable computing devices, such as glasses, watches, or sensors embedded into your clothing, are obtrusive. Jason Hong, associate professor of computer science at Carnegie Mellon University, wrote in a 2014 co-authored article in Pervasive Computing that while wearables gather input from sensors placed optimally on our bodies, they can also be "harder to accommodate due to our social context and requirements to keep them small and lightweight."

The SEI Blog continues to attract an ever-increasing number of readers interested in learning more about our work in agile metrics, high-performance computing, malware analysis, testing, and other topics. As we reach the mid-year point, this blog posting highlights our 10 most popular posts, and links to additional related resources you might find of interest (Many of our posts cover related research areas, so we grouped them together for ease of reference.)

Before we take a deeper dive into the posts, let's take a look at the top 10 posts (ordered by number of visits, with #1 being the highest number of visits):

This is the second installment of two blog posts highlighting recommended practices for developing safety-critical systems that was originally published on the Cyber Security & Information Systems Information Analysis Center (CSIAC) website. The first postin the series by Peter Feiler, Julien Delange, and Charles Weinstock explored challenges to developing safety critical systems and presented the first three practices:

  1. Use quality attribute scenarios and mission-tread analyses to identify safety-critical requirements.
  2. Specify safety-critical requirements, and prioritize them.
  3. Conduct hazard and static analyses to guide architectural and design decisions.

Software and acquisition professionals often have questions about recommended practices related to modern software development methods, techniques, and tools, such as how to apply agile methods in government acquisition frameworks, systematic verification and validation of safety-critical systems, and operational risk management. In the Department of Defense (DoD), these techniques are just a few of the options available to face the myriad challenges in producing large, secure software-reliant systems on schedule and within budget.

Using the Architecture Analysis & Design Language (AADL) modeling notation early in the development process not only helps the development team detect design errors before implementation, but also supports implementation efforts and produces high-quality code. Our recent blog posts and webinar have shown how AADL can identify potential design errors and help avoid propagating them through the development process, where remediation can require massive re-engineering, delay the schedule, and increase costs.

This post is the first in a series introducing our research into software and system complexity and its impact in avionics.

On July 6, 2013, an Asiana Airlines Boeing 777 airplane flying from Seoul, South Korea, crashed on final approach into San Francisco International airport. While 304 of the 307 passengers and crew members on board survived, almost 200 were injured (10 critically) and three young women died. The National Transportation Safety Board (NTSB) blamed the crash on the pilots, but also said "the complexity of the Boeing 777's auto throttle and auto flight director--two of the plane's key systems for controlling flight--contributed to the accident."