As with any new initiative or tool requiring significant investment, the business value of statistically-based predictive models must be demonstrated before they will see widespread adoption. The SEI Software Engineering Measurement and Analysis (SEMA)initiative has been leading research to better understand how existing analytical and statistical methods can be used successfully and how to determine the value of these methods once they have been applied to the engineering of large-scale software-reliant systems.
The DoD relies heavily on mission- and safety-critical real-time embedded software systems (RTESs), which play a crucial role in controlling systems ranging from airplanes and cars to infusion pumps and microwaves. Since RTESs are often safety-critical, they must undergo an extensive (and often expensive) certification process before deployment. This costly certification process must be repeated after any significant change to the RTES, such as migrating a single-core RTES to a multi-core platform, significant code refactoring, or performance optimizations, to name a few.
As part of an ongoing effort to keep you informed about our latest work, I'd like to let you know about some recently published SEI technical reports and notes. These reports highlight the latest work of SEI technologists in Agile methods, insider threat,the SMART Grid Maturity Model, acquisition, and CMMI. This post includes a listing of each report, author/s, and links where the published reports can be accessed on the SEI website.
Whether soldiers are on the battlefield or providing humanitarian relief effort, they need to capture and process a wide range of text, image, and map-based information. To support soldiers in this effort, the Department of Defense (DoD) is beginning to equip soldiers with smartphones to allow them to manage that vast array and amount of information they encounter while in the field. Whether the information gets correctly conveyed up the chain of command depends, in part, on the soldier's ability to capture accurate data while in the field. This blog posting, a follow-up to our initial post, describes our work on creating a software application for smartphones that allows soldier end-users to program their smartphones to provide an interface tailored to the information they need for a specific mission.
Cloudlets, which are lightweight servers running one or more virtual machines (VMs), allow soldiers in the field to offload resource-consumptive and battery-draining computations from their handheld devices to nearby cloudlets. This architecture decreases latency by using a single-hop network and potentially lowers battery consumption by using WiFi instead of broadband wireless. This posting extends our original postby describing how we are using cloudlets to help soldiers perform various mission capabilities more effectively, including facial, speech, and imaging recognition, as well as decision making and mission planning.
Cyber-physical systems (CPS) are characterized by close interactions between software components and physical processes. These interactions can have life-threatening consequences when they include safety-critical functions that are not performed according to their time-sensitive requirements. For example, an airbag must fully inflate within 20 milliseconds (its deadline) of an accident to prevent the driver from hitting the steering wheel with potentially fatal consequences. Unfortunately, the competition of safety-critical requirements with other demands to reduce the cost, power consumption, and device size also create problems, such as automotive recalls, new aircraft delivery delays, and plane accidents.
Malware, which is short for "malicious software," is a growing problem for government and commercial organizations since it disrupts or denies important operations, gathers private information without consent, gains unauthorized access to system resources, and other inappropriate behaviors. A previous blog postdescribed the use of "fuzzy hashing" to determine whether two files suspected of being malware are similar, which helps analysts potentially save time by identifying opportunities to leverage previous analysis of malware when confronted with a new attack. This posting continues our coverage of fuzzy hashing by discussing types of malware against which similarity measures of any kind (including fuzzy hashing) may be applied.
Each year, the CERT Division of the SEI collaborates with CSO Magazine to develop a U.S. State of Cybercrime report1. These reports are based on surveys of more than 500 organizations across the country, ranging in size from fewer than...