Businesses across the globe are evolving each day to confront the market challenges and deal with maturing customer expectations. Even the ongoing technology advancements keep twisting the arms of software testing experts for more attention and precision in the practice and processes. The year 2018 brought into its fold new technology solutions and enablement for Quality Assurance and Software Testing. At the same time, practices such as Agile, DevOps, DevSecOps, and Test Automation continued to sustain its relevance and application through the software testing cycle.
\nAs the year ends, we can gauge some strong trends that are expected to rule the software testing and development scene in 2019. We have shortlisted prominent trends and we would be glad to get your views and value additions on this post as well.<\/p>\n
A Forbes feature<\/a> titled \u2018AI In Software Testing: Will A Bot Steal Your Spot?\u2019 mentions, \u2018The trend of relying on technology for highly repetitive tasks while enabling people to focus on high-value activities, such as revenue generation, relationship building and growth management, is speeding up the pace of change. Because much of testing space is repetitive, I believe AI can easily be utilized in those areas. The remainder will be left to the testers whose job will be to scrutinize the system and help work alongside AI to revolutionize the way testing is executed.\u2019 A lot has been extensively discussed and written about Digital Transformation during the last one year. Businesses are going through tremendous digital change resulting in numerous insecurities and challenges as well. With methodologies such as Agile and DevOps, the testing process has become much more flexible and responsive to the business needs. Nevertheless, the need for Continuous Testing, Deployment, and Integration will only increase as the business evolves further. As business experts and technology geeks have estimated, Data will empower business decisions and enable effective decision making capabilities. However, it is important to test the data and ensure that the inferences derived are accurate and delivered in a format that is easily comprehensible. Testers need to verify that terabytes of data is effectively processed and broken down into precise clusters to derive the much needed inferences. As per a report by ResearchAndMarkets, \u2018the Global Intelligent apps market accounted for $8.39 billion in 2017 and is expected to reach $93.4 billion by 2026 growing at a CAGR of 30.7% during the forecast period. Growing desire for advanced analytical tools, advancements in technology on deploying new product, and heightened market for big data & analytics are fuelling the intelligent apps. Deepened acceptance among developing economies offers a major opportunity for market expansion.\u2019<\/p>\n Reports such as these sum up the need for Intelligent Apps and as we can evaluate from the upcoming trends, the requirement will only increase. However, there is a growing need for effectively testing these applications for accuracy, performance, security, functionality, and anything that could be need based.<\/p>\n Ensuring that your application or software works as expected under changing or challenging conditions will always be an important factor to consider. Performance Testing<\/a> has always been a critical aspect of the software testing strategy. However, as the trend goes, Performance Testing will eventually shift gears towards Performance Engineering. The focus will be predominantly on all the factors that work cohesively to ensure performance, security, usability, network compatibility, and much more.<\/p>\n Hence, all these factors will have to work towards delivering a high-quality application. The reason being, these aspects are needed to deliver ever-increasing customer expectations through 2019.<\/p>\n
\nThis kind of paraphrases the need and significance of AI in Software Testing. Application of AI in Software Testing for speed and accuracy is bound to increase as we move faster with digital transformation of businesses. Even Machine Learning is making strides in testing and software development processes, especially with Predictive Analytics, Log Analytics, Requirements Traceability Matrix, and Defect Analytics.<\/p>\nDigital Transformation with Continuous Integration<\/h2>\n
\nNew features will have to be delivered in a gradual pattern, which will intensify the need for Continuous deployment and Integration within the up and running applications. Business Transformation is bound to throw new challenges each day, which could be related to Performance, Security, or Functionality. Hence, the need for Continuous Development and Integration will only increase as time passes.<\/p>\nTesting Data for effective application within businesses<\/h2>\n
\nThis testing could range from performance testing to functional testing and even security testing. Ensuring the quality of data is highly critical in big data testing, which can be confirmed on the basis of characteristics such as precision, accuracy, consistency, and duplicity, to begin with.<\/p>\nTesting Intelligent Apps<\/h2>\n
Performance Engineering and not just Performance Testing<\/h2>\n