It’s a given for web and mobile application development that performance testing is critical to application success. Yet, for organizations wishing to customize third-party (3P) software as a service (SaaS) rather than running it out of the box as written, performance testing can be problematic.
As most organizational leaders recognize at some level, the increasing dominance of web and mobile applications has completely turned the software world on its ear. The number of critical business functions that are processed via a browser or mobile device is escalating, and inaccurate results, aberrant behaviors, and security flaws can all be absurdly costly.
In today’s threat-laden environment, where production data is one of the chief targets of hackers, organizations developing software must expend both time and resources securing their production data. One of the simplest ways to ensure security for software testing activities is through the use of targeted, advanced data-handling solutions that can synthesize and virtualize production data.
Despite the proven value of collecting raw software project data and analyzing it to create actionable, easily digested key performance indicators (KPIs), many firms still struggle to extract, analyze, and organize this data into reports—let alone dashboards or scorecards. If this sounds like your organization, don’t be surprised. In my experience, only a handful of firms have been able to implement truly effective, streamlined reporting systems—and many teams and their leaders don’t even recognize what they are missing.
“The world has really changed, as we see it. You used to have a point where an application was ready to go live and security would have to come in and bless the application….What we see now is that is not the [case]. The focus now is to ensure we have an ecosystem to make secure applications and monitor…so you don’t have to stop on your way out the door.”
Although both mobile apps and big data analytics have become pivotal to business operations on a broad scale, one area of software development—enterprise resource planning (ERP)—has not historically been part of the revolution. Today, that is changing, as ERP developers recognize that their systems are perfect candidates for mobile-centric, big data analytics.
Mobile user expectations are an interesting thing. We all hear that they are through the roof, and that users expect app behaviors and responses to occur in near-nanoseconds. What most of us don’t hear is that the features and functions that matter to users do not necessarily align with what developers are giving them—or what developers think they want.
As user expectations escalate and development and testing costs continue to increase, organizations are seeking additional mechanisms for gaining more insight, earlier, to improve product quality. One contributor to this effort is data analytics and visualization.
Thanks to the handful of organizations that are producing amazing apps, users expect more. Systems and transaction chains are more complex, the number of devices continues to grow, and users want to do more, but with fewer actions and less decision making. What are software teams to do? How do you answer the challenge?
It does little good for a developer to expend thousands of hours and enormous sums of money developing and testing a gorgeous, brilliantly functional app if security ends up being an issue. This article explores the current landscape and offers some best practices that developers—both third-party developers and corporate teams—can adopt to foster security and confidence in their mobile apps.
Switching from a legacy system to a composite application? It can be tricky, because the transition requires a lot of restructuring. To be sure you’re conducting the most streamlined, complete transfer possible, you need to focus on key performance indicators.
In software engineering, if metrics and KPIs are being confused or used interchangeably, it generally means that stakeholders are not defining and building out KPIs, and software teams are not using them effectively. At the end of the day, these incorrect assumptions diminish the quality and amount of actionable information that can help reduce defects and promote a better outcome.