Due to advances in hardware and software technologies, Department of Defense (DoD) systems today are highly capable and complex. However, they also face increasing scale, computation, and security challenges. Compounding these challenges, DoD systems were historically designed using stove-piped architectures that lock the Government into a small number of system integrators, each devising proprietary point solutions that are expensive to develop and sustain over the lifecycle. Although these stove-piped solutions have been problematic (and unsustainable) for years, the budget cuts occurring under sequestration are motivating the DoD to reinvigorate its focus on identifying alternative means to drive down costs, create more affordable acquisition choices, and improve acquisition program performance. A promising approach to meet these goals is Open Systems Architecture (OSA), which combines
modular open development practices designed to reduce the cycle time needed to acquire new systems and insert new technology into legacy systems, and
This blog posting expands on earlier coverage of how acquisition professionals and system integrators can apply OSA practices to effectively decompose large monolithic business and technical architectures into manageable and modular solutions that can integrate innovation more rapidly and lower total ownership costs.
By David Svoboda Senior Member of the Technical Staff CERT Division
Whether Java is more secure than C is a simple question to ask, but a hard question to answer well. When we began writing the SEI CERT Oracle Coding Standard for Java, we thought that Java would require fewer secure coding rules than the SEI CERT C Coding Standard because Java was designed with security in mind. We naively assumed that a more secure language would need fewer rules than a less secure one. However, Java has 168 coding rules compared to just 116 for C. Why? Was our (admittedly simplistic) assumption completely spurious? Or, are there problems with our C or Java rules? Or, are Java programs, on average, just as susceptible to vulnerabilities as C programs? In this post, I attempt to analyze our CERT rules for both C and Java to determine if they indeed refute the conventional wisdom that Java is more secure than C.
There are more than 200 different types of testing, and many stakeholders in testing--including the testers themselves and test managers--are often largely unaware of them or do not know how to perform them. Similarly, test planning frequently overlooks important types of testing. The primary goal of this series of blog posts is to raise awareness of the large number of test types, to verify adequate completeness of test planning, and to better guide the testing process. In the previous blog entry in this series, I introduced a taxonomy of testing in which 15 subtypes of testing were organized around how they addressed the classic 5W+2H questions: what, when, why, who, where, how, and how well. This and future postings in this series will cover each of these seven categories of testing, thereby providing structure to roughly 200 types of testing currently used to test software-reliant systems and software applications.
This blog entry covers the four, top-level subtypes of testing that answer the following questions:
What-based testing: What is being tested?
Object-Under-Test-based (OUT) testing
When-based testing: When is the testing being performed?
Built-in-Test (BIT) testing
After exploring the what-based and when-based categories of testing, this post presents a section on using the taxonomy, as well as opportunities for accessing it.
By Julien Delange Member of the Technical Staff Software Solutions Division
For decades, safety-critical systems have become more software intensive in every domain--in avionics, aerospace, automobiles, and medicine. Software acquisition is now one of the biggest production costs for safety-critical systems. These systems are made up of several software and hardware components, executed on different components, and interconnected using various buses and protocols. For instance, cars are now equipped with more than 70 electronic control units (ECUs) interconnected with different buses and require about 100 million source lines of code (SLOC) to provide driver assistance, entertainment systems, and all necessary safety features, etc. This blog post discusses the impact of complexity in software models and presents our tool that produces complexity metrics from software models.
While evaluating the test programs of numerous defense contractors, we have often observed that they are quite incomplete. For example, they typically fail to address all the relevant types of testing that should be used to (1) uncover defects (2) provide evidence concerning the quality and maturity of the system or software under test, and (3) demonstrate the readiness of the system or software for acceptance and being placed into operation. Instead, many test programs only address a relatively small subset of the total number of potentially relevant types of testing, such as unit testing, integration testing, system testing, and acceptance testing. In some cases, the missing testing types are actually performed (to some extent) but not addressed in test-related planning documents, such as test strategies, system and software test plans (STPs), and the testing sections of systems engineering management plans (SEMPs) and software development plans (SDP). In many cases, however, they are neither mentioned nor performed. This blog, post, the first in a series on the many types of testing, examines the negative consequences of not addressing all relevant testing types and introduces a taxonomy of testing types to help testing stakeholders understand--rather than overlook--them.
As Soon as Possible In the first post in this series, I introduced the concept of the Minimum Viable Capability (MVC). While the intent of the Minimum Viable Product (MVP) strategy is to focus on rapidly developing and validating only...