The differences in implementations are usually due to the presence of context-specific elements embedded in the code. This makes the abstractions hard to reuse in other contexts; hence, reusability of the abstractions is impacted. Reliability—When an abstraction “leaks” internal data structures, the integrity of the abstraction may be compromised, leading to runtime problems. Reusability—Clients of the abstraction with Leaky Encapsulation smell may directly depend upon the implementation details of the abstraction.
Learnability is one of the five quality components of usability (the others being efficiency, memorability, errors, and satisfaction). Testing learnability is especially valuable for complex applications and systems that users access frequently, though knowing how quickly users can acclimate to your interface is valuable for even objectively simple systems. Take it one step further, and you’ll find that your software’s complexity is derived from a large amount of developers writing a lot of code. Simply put, the more code that’s written, the more complex and interdependent it becomes. It becomes harder to comprehend its behavior and, unfortunately, much more difficult to get the data you need from that code to help you understand what’s going on in there.
In some cases, you were knowledgeable about the software and quickly and efficiently carried out tasks. Things might have been more challenging in other places, with jobs requiring help from other engineers and sometimes even requiring some back and forth to get it right. If you have a clear mental image of the application in your mind, and high-quality data is available to you, you can expect to perform reasonably well in all of those tasks. You’ll be able to make faster and better decisions in every situation and are much more likely to be able to resolve it yourself. If you have a poor understanding of the application, you won’t likely perform as well.
For every characteristic, there are a set of questions which are relevant to that characteristic. Some type of scoring formula could be developed based on the answers to these questions, from which a measurement of the characteristic can be obtained. The software build is critical to software quality because if any of the generated files are incorrect the software build is likely to fail. And, if the incorrect version of a program is inadvertently used, then testing can lead to false results.
2.5 Impacted Quality Attributes
For example, data analysts will most likely use SQL to build reports and dashboards. Data scientists, in turn, expect data to come in a file-based structure to train artificial intelligence models. MDPs are state-based stochastic models, where actions can be performed in states to switch to a successor states following an action-dependent probabilistic distribution. Usually, the probabilistic distribution models several aspects of the environment, whereas actions correspond to decisions of the program or the user. For more details on stochastic operational models, we refer to standard textbooks such as  (check also for Chapter 8.3.1).
David was going to deal with the metrics for FRG internal and external quality characteristics (according to ISO/IEC , ISO, 2003a, and ISO/IEC , ISO, 2003b), in particular I/O utilization, memory utilization, and response time. Sean restricted himself to quality in use metrics (according to ISO/IEC ; ISO, 2004), namely FRG task efficiency. After negotiation, https://www.globalcloudteam.com/ David agreed not to discuss I/O utilization and memory utilization; whereas Sean agreed to discuss response time instead of task efficiency because this external quality metric was understandable to him. For example, let’s say we are redesigning an enterprise file-backup application intended to be run by IT administrators on a regular basis.
That led to an enormous token, resulting in too many authentication attempts, and finally, that infamous error message. Within that safety check, there was a TODO comment to add a logline for this abnormal situation. Nothing is more painful than the logline that should have been there but wasn’t. They had inadvertently passed a flag requesting Google to include the full user profile within the login token. Later in the code, they had a safety check, trimming the token above a certain length and rendering it invalid.
- Ultimately, to “make the product talk”, as Dieter Rams puts it, you should focus on a clear, intuitive interface that will convey the brand’s look & feel at first glance.
- For instance, if a banking application implements a checking account service that does not implement a deposit function but instead relies on the client to use a separate deposit service, this would detract from the service’s modular understandability.
- In the nonprobabilistic setting, model-checking approaches are prominent for generating counterexamples as a byproduct of the verification process .
- As it turned out, one of their engineers could not log on to that application.
- Understandability is empowering your everyday efforts, and poor understanding is painfully apparent, especially when it comes to incident response and bug resolution.
- The second one focuses on the ability to alert the dev team when the system misbehaves and help them identify the causes of any problems, so that normal service can be restored.
The more you understand the application you are responsible for, the easier it is to put the pieces together. The easier it is to collect the information you need and the less information you will need to see the bigger picture. The less you understand, the more you will find yourself relying on other people, struggling to collect more information. If all else fails, you just may find yourself pushing log lines to production to try and figure out what is going on. We all had that eureka moment where a colleague approached us to report a bug, and even before we finished the sentence, we knew what was wrong and in which line of code should fix it.
On the one hand, we always knew that keeping code easy to read and maintain was important and that much of software engineering was focused on that feat. And yet, we now have a greater appreciation for how much of a difference it can make in allowing software to grow and evolve over time. Last but not least, make sure to have the scaffolding in place to deal with complexity when it arises.
For probabilistic and dynamic feature-oriented systems, Dubslaff et al. [1005,1006] introduced a compositional modeling and analysis framework. Here, operational behaviors of features are described through MDPs with further feature information. The dynamic activation and deactivation of features can be used to describe adaptive behaviors of feature-oriented systems, e.g., to model adaptive heterogeneous hardware systems  and context-dependent systems [1007,1008].
Systems can often be reduced from the “bottom” as well, by using higher-level abstractions, such as new programming languages, advanced frameworks, and modern databases. In the context of software engineering, software quality measures how well software is designed (quality of design), and how well the software conforms to that design (quality of conformance), although there are several different definitions. The learnability of a product tells us how fast users reach optimal behavior with that product. It is important to measure learnability for UIs that get used relatively frequently. A learnability study involves repeated measurements of the same participants completing the same task.
Counterexamples are likely to contain an event that causes the property violation. They are usually provided as error traces or in a tree-like structure to guide through the systems source code or operational behavior. In the nonprobabilistic setting, model-checking approaches are prominent for generating counterexamples as a byproduct of the verification process . The generation of counterexamples in the probabilistic setting is, however, more involved [987–989]. In , Leitner-Fischer and Leue describe an approach to generate counterexamples based on event variables in a specialized event-order logic and use them to reason about causality, implemented in the tool SpinCause .
Initially, David produced three FRG prototypes (starting from 1 ms response time) until he felt that the response time of 0.25 ms was good. Then he showed the prototype to Sean, who did not like it and felt this response time was bad. Initially, David considered also the Database Subsystem (DBS) and the Hardware Subsystem (HWS) as relevant artifacts, because the quality of FRG depended on their characteristics. Sean, however, only dealt with FRG because he did not see the other components of the system. As a result of negotiation, David agreed not to discuss DBS and HWS with Sean.
There are four key characteristics that need to be present in order to obtain understandability into your software. Reliability—When two abstractions have identical names, a confused developer may end-up using the wrong abstraction. In case of abstractions with identical implementations, a modification in one of the abstractions needs to be duplicated across the other copies failing which a defect may occur. Changeability and Extensibility—Change or enhancement involving one abstraction potentially requires making the same modification in the duplicate abstractions as well. Runtime reliability determinations are similar to tests, but go beyond simple confirmation of behaviour to the evaluation of qualities such as performance and interoperability with other code or particular hardware configurations. These are quite often technology-related and depend heavily on the context, business objectives and risks.
For an app to achieve understandability, UX must be taken into account as well. The application should be easily used by users without them being disoriented and confused. We now have a better perspective on the importance of Understandability in software development.