Contents • • • • • • • • • • Introduction [ ] In general, building robust systems that encompass every point of possible failure is difficult because of the vast quantity of possible inputs and input combinations. Since all inputs and input combinations would require too much time to test, developers cannot run through all cases exhaustively.

Sep 12, 2014 - 14 min - Uploaded by Soundarya KasturiOne of the problems that exist is poor quality that people send and use. This causes problems to. Feb 26, 2014. Why code quality really matters: Part 1 - Robust programming for ABAP. One of the unpleasant issues with (business) software is that it has the nasty tendency to fail when users least expect it. But most of the time such failure isn't the result of unforeseeable circumstances. It's just the result of unforeseen.

Instead, the developer will try to generalize such cases. For example, imagine inputting some. Some selected inputs might consist of a negative number, zero, and a positive number.

When using these numbers to test software in this way, the developer generalizes the set of all reals into three numbers. Autocad 2007 Free Download Full Version With Crack For Windows 7 Torrent. This is a more efficient and manageable method, but more prone to failure.

Generalizing test cases is an example of just one technique to deal with failure—specifically, failure due to invalid user input. Systems generally may also fail due to other reasons as well, such as disconnecting from a network. Regardless, complex systems should still handle any errors encountered gracefully.

There are many examples of such successful systems. Some of the most robust systems are evolvable and can be easily adapted to new situations. Challenges [ ] Programs and software are tools focused on a very specific task, and thus aren't generalized and flexible.

Light Processor Q12 Manual Transmission. However, observations in systems such as the or demonstrate adaptation to their environments. One of the ways biological systems adapt to environments is through the use of redundancy.

Many organs are redundant in humans. The is one such example.

Generally only need one kidney, but having a second kidney allows room for failure. This same principle may be taken to apply to software, but there are some challenges. When applying the principle of redundancy to computer science, blindly adding code is not suggested. Blindly adding code introduces more errors, makes the system more complex, and renders it harder to understand. Code that doesn't provide any reinforcement to the already existing code is unwanted.

The new code must instead possess equivalent [ ], so that if a function is broken, another providing the same function can replace it. To do so, the new code must know how and when to accommodate the failure point. This means more needs to be added to the system. But as a system adds more logic,, and increases in size, it becomes more complex. Thus, when making a more redundant system, the system also becomes more complex and developers must consider balancing redundancy with complexity.

Currently, computer science practices do not focus on building robust systems. Rather, they tend to focus on and.

One of the main reasons why there is no focus on robustness today is because it is hard to do in a general way. Areas [ ] Robust programming [ ] Robust programming is a style of programming that focuses on handling unexpected termination and unexpected actions.

It requires code to handle these terminations and actions gracefully by displaying accurate and unambiguous. These error messages allow the user to more easily debug the program. Principles [ ] Paranoia - When building software, the programmer assumes users are out to break their code. The programmer also assumes that his or her own written code may fail or work incorrectly. Stupidity - The programmer assumes users will try incorrect, bogus and malformed inputs. As a consequence, the programmer returns to the user an unambiguous, intuitive error message that does not require looking up error codes.

The error message should try to be as accurate as possible without being misleading to the user, so that the problem can be fixed with ease. Dangerous implements - Users should not gain access to,, or to data structures. This information should be hidden from the user so that the user doesn't accidentally modify them and introduce a bug in the code. When such are correctly built, users use them without finding loopholes to modify the interface. The interface should already be correctly implemented, so the user does not need to make modifications. The user therefore focuses solely on his or her own code. - Very often, code is modified and may introduce a possibility that an 'impossible' case occurs.

Impossible cases are therefore assumed to be highly unlikely instead. The developer thinks about how to handle the case that is highly unlikely, and implements the handling accordingly. Robust machine learning [ ] Robust machine learning typically refers to the robustness of machine learning algorithms. For a machine learning algorithm to be considered robust, either the testing error has to be consistent with the training error, or the performance is stable after adding some noise to the dataset. Robust network design [ ] Robust network design is the study of network design in the face of variable or uncertain demands. In a sense, robustness in network design is broad just like robustness in software design because of the vast possibilities of changes or inputs.

Examples [ ] • See also [ ] • • References [ ]. Retrieved 2016-11-13.

IEEE Standard Glossary of Software Engineering Terminology, IEEE Std 6 defines robustness as 'The degree to which a system or component can function correctly in the presence of invalid inputs or stressful environmental conditions' • (PDF). Retrieved 2016-11-13. • ^ Gerald Jay Sussman (January 13, 2007). Retrieved 2016-11-13. • Joseph, Joby (2009-09-21).. Software Testing Club. Retrieved 2016-11-13.

• Agents on the wEb: Robust Software. Retrieved 2016-11-13. Retrieved 2016-11-13. • El Sayed Mahmoud.. Retrieved 2016-11-13.

Retrieved 2016-11-13.

Chapter 10 Generic Programming and Collection Classes How to avoid reinventing the wheel? Many data structures and algorithms, such as those from, have been studied, programmed, and re-programmed by generations of computer science students. This is a valuable learning experience. Unfortunately, they have also been programmed and re-programmed by generations of working computer professionals, taking up time that could be devoted to new, more creative work. A programmer who needs a list or a binary tree shouldn't have to re-code these data structures from scratch. They are well-understood and have been programmed thousands of times before. The problem is how to make pre-written, robust data structures available to programmers.

In this chapter, we'll look at Java's attempt to address this problem. You have already seen part of the solution in. That section introduced parameterized types and the ArrayList class. Parameterized types make it possible for the same class to work with many different kinds of data. This idea -- that the same code can be used for a variety of data types -- is called generic programming. Contents of Chapter 10: • Section 1: • Section 2: • Section 3: • Section 4: • Section 5: • •.