Warranty Management Embedded With Business Intelligence and Analytics: A Boon for Warranty Stakeholders!
Today’s leading Software Consumers are becoming increasingly keen on Business Intelligence and Analytical tools that can help transform the raw data into actionable insights. Data and insights are two very different things when it comes to Warranty Service or rather, any service. Companies do collect huge volumes of Warranty data, but they fail to convert it into insightful information that would allow timely and proactive actions from the manufacturer. With proper insights, potential Warranty problems can be handled at an early stage thereby resulting in reduced the warranty costs, improved product quality and higher customer satisfaction. With the presence of a warranty management solution, manufacturers are able to run their warranty operations but are unable to utilize the informative data available in hand because of the absence of analytics. This is where the Business Intelligence and Analytics step in, and take Warranty Management operations to the next level. Embedded BI or Analytics refer to capabilities such as enhanced Reporting, Dashboards, Data Discovery, or Data Management capabilities included as an integrated module or an extension of existing Software. With embedded Business Intelligence and Analytics, the stakeholders will get a holistic view of the entire Warranty Lifecycle. Some of the potential problems and cost drivers in warranty like fraudulent/duplicate claims, Rule Processing Engine, Failures, Field Modifications, and Supplier Management are better tackled by deploying Analytics. Studying the analytical data and having a snapshot of key Performance Indicators on these would allow the stakeholders to solve the problem with a preventive approach rather than a reactive one. For instance, by digging or zeroing down to the list of failures that occur most frequently (Viz. Top 5 Failure Areas), would help the engineering team to solve issues during the early stages at the manufacturing plant, hence decreasing the warranty cost incurred. Warranty Engineers may refine the Processing Rules by analyzing the Top 10 Rule Failures leading to further decrease in manual reviews. With Analytics, the stakeholder will have better information on fraudulent/duplicate claims. Business Intelligence and enhanced Analytics on Warranties provide insights to functions across the organization like Sales, Marketing, Design and Engineering. ‘Patterns and Trends’, the striking feature of Analytics guides cross-functional departments in an organization in better forecasting, accruals and reserves. We can say that it not only enables manufacturers to take preventive measures but also helps in predicting risks and failures by analyzing history and trends. The Strength of Intelligent Warranty Data Warranty Management Data and its logical analysis play a crucial role across various business functions of an organization. It helps to understand product performance in the field, top warranty cost drivers, field service issues, as well as the serviceability and reliability of products. In the absence of a central Warranty Business Intelligence system, it becomes difficult to analyze the product effectively. Hence, improvement initiatives cannot be triggered on time. The figure above shows how the best in class organizations have been effectively using warranty data to improve cross-functional performances, but some of the manufacturing brands still have a long way to go in effectively measuring and analyzing warranty data and converting it into actionable insight World-class organizations are addressing the risks by implementing Business Intelligence and Data Warehousing systems. The takeaway is that an investment in Warranty Management embedded with Business Intelligence systems is a long-term beneficial investment in the form of Quality Products, Better Field Service and greater customer satisfaction.
When Reaching the End Means More Than Being First.
Over the last 5 years or so, a lot has happened in the IT industry that has disrupted the very way we use hardware and software, and to an extent, it has given us alternate ways to consume information. Every year, analyst firms and IT service providers look for technologies or trends that will drive the next wave of change (or disruption). This year too, things are no different–with some overlapping trends in the numerous predictions and insights that are coming our way. Overall, two areas stand out in having a profound impact on the way technology will be strategized and applied to consumers and the enterprise. First comes Mobility, and then comes the Disruptive Cloud. The interesting point to note is that both these forces are mutually reinforcing the other as they evolve. All the leading IT solution and service providers today, have increased their focus on the mobility segment– which includes the mobile, notebook, and everything in between. The Cloud, on the other hand, has enabled the realization of the unthinkable–to enable processing of data on devices, no matter what operating system or hardware it is running on. So anybody would agree with me when I say that the Cloud now controls the digital lives of people and extends anywhere from computing to communicating. If the recent product and service launches are to be analyzed, the signals are clear that the primary goal of IT solution providers is to create a powerful ecosystem from both the developer and consumer perspectives. The release of windows 8 is a perfect example in this regard, as it is in line with the strategy being adopted by the big players in the IT domain. In a nutshell, Windows 8 is the older version in a new bottle with some features taken to the visual backend and up come those Apps! Apps that have evolved with new usability features and behavior. Though they require a multi-channel integration and interaction, the end product is so advanced that the experience can be customized to where a person is located and what they are doing. This is the same strategy being followed by Google, though not in very obvious terms, with Apps being designed for mobile and bigger devices. However, even though both the strategies might be the same in a way, the business sense is entirely different. Both Microsoft and Google are at the two ends of the OS dominance. One rules the desktop space while the other is at the mobile and both want more. For Microsoft it is about leveraging their desktop OS superiority to the not so successful mobile space while for Google there seems to be a simpler challenge – develop more for mobile and then leverage the same for larger devices. But hey, why are we not taking Apple into consideration here? Because, looks like they ‘bit the fruit’ first and did not feel anything! This strategy worked out perfectly for Apple with their scalable OS that works great on all devices. Though mobile and cloud came later, they were ready to embrace the change and leverage it to their strengths, even though they metaphorically, ‘arrived late to the party’! If you watched the WWDC2013 keynote, all this would make perfect sense as Apple lays out the plan for the next 10 years. The fight on the other hand for Microsoft and Google is not about reaching first or about dominance but it is about who reaches the other end first. And what will help them achieve this – mobility and cloud. Either way, ‘biting the fruit like Apple’ second causes lesser pain, doesn’t it?
Performance Monitoring for Best-in-Breed Mobile Apps
My smartphone is now an indispensable part of me! I reckon any technology enthusiast or a business decision maker would say the same. We all know that the smartphone space has seen exponential growth in the last few years. Companies today are increasingly relying on the mobile medium; not only as a powerful way to engage customers, but also as a tool to address their day-to-day business needs. Why do you feel this paradigm shift happened? Well, I guess it’s a no-brainer for the matured mobile user! It’s just the realization that mobile apps can add significant value to the success of a business. Hence, everyone (from individuals to organizations) is striving to deliver more value by providing new and innovative mobile apps. As mobile applications gain importance, application performance monitoring and its related tools are also passing through a phase of transformation. Today’s mobile teams are realizing that they can’t manage mobile apps with traditional web technologies. Mobile application performance monitoring tools are providing greater thrust towards application stability in the mobile app paradigm. Application performance monitoring/crash analytics delivers a capability to provide insights about mobile apps to investigate concerns such as: > Is the OS update causing a problem? > Is the device or network causing problems? > Is the OS version and hardware causing issues? > App health and availability – Do they matter? > App improvement through performance boosting and code testing This allows the application performance monitoring tools to enhance quality of mobile applications by providing detailed analysis on: > Crash Data > Crash Trends > Lines of code causing crash > MAU / DAU Tracking > Handled Exceptions > OS version & device using app > Error Monitoring – Diagnostics and Live Graphs > Network Monitoring – Performance of outside cloud services and network conditions Today’s application providers are integrating application performance monitoring tools to capture and analyze the application performance footprints more efficiently. Mobile application development that leverages application performance monitoring/crash analytics tools is now enabling the development of improved and stable apps. This impact is being realized by the increase in revenue through the apps. The notable names, as per VisionMobile 2013, in the Application Performance Monitoring space include BugSense, Crittercism and TestFlight. All these companies have their own USPs, and provide their services across varied mobile platforms. The various mobile platforms supported by each of these vendors are listed below. Though the mobile performance monitoring space is growing fast, the domain is still in its early stages of development. Many new ventures are coming up with their innovative solutions. Companies such as Google and Twitter are investing in these ventures to provide thrust to these much-needed application-monitoring solutions. The first half of 2013 has seen a lot of venture funding and acquisitions; a few are listed below: > Crittercism received $12M from Google ventures, and more to help mobile developers monitor app, network performance – March-2013 > Twitter scooped up its crash-reporting competitor Crashlytics in January 2013 > Newcomers like Torbit and Bugsnag have been able to find traction almost from the get-go – 2013 > Compuware launched free native mobile application performance monitoring service – April 2013 These recent developments in the mobile app space clearly indicate a major thrust in the field of mobile application performance monitoring tools. Though only a few companies have moved into the space to gain the first mover advantage, the area is certainly going to see lot of traction in the coming years. I would like to conclude by citing that the interest shown by both large as well as small companies, along with the increased funding, will certainly boost this fast-evolving segment. FAQs – Tavant Solutions How does Tavant ensure optimal performance monitoring for lending mobile applications?Tavant implements comprehensive mobile app performance monitoring through real-time analytics, automated error detection, performance benchmarking, and user experience tracking. Their monitoring solutions provide detailed insights into app performance, user behavior, and system health to ensure optimal mobile lending experiences. What mobile app performance metrics does Tavant track for lending applications?Tavant tracks app load times, transaction completion rates, error frequencies, user engagement metrics, crash reports, and system resource utilization. Their monitoring platform provides dashboards and alerts that enable proactive performance optimization and rapid issue resolution for mobile lending apps. What are the key performance metrics for mobile lending apps?Key performance metrics include app load time, transaction success rates, user session duration, conversion rates, crash frequency, API response times, and user satisfaction scores. These metrics help ensure smooth, efficient mobile lending experiences that meet customer expectations. How do you monitor mobile app performance effectively?Effective mobile app performance monitoring involves real-time analytics tools, automated error tracking, user experience monitoring, performance benchmarking, and regular testing across different devices and network conditions. Continuous monitoring enables proactive optimization and issue prevention. Why is mobile app performance critical for lending?Mobile app performance is critical for lending because poor performance leads to application abandonment, customer frustration, lost revenue, and competitive disadvantage. Fast, reliable mobile experiences are essential for customer acquisition, retention, and successful loan origination in today’s mobile-first market.
Video Ad Serving Via VAST
Historically, different proprietary video players used by different publishers had quite a few technical complexities, and thereby limiting the reach of digital video advertising campaigns. To resolve this matter, the Interactive Advertising Bureau (IAB) introduced an XML-based specification known as VAST (Video Ad Serving Template). In my opinion, it’s a disruptive innovation that transformed the video advertising landscape. VAST facilitated interoperability by having a common in-stream advertising protocol for video players. The protocol is based on an universal XML schema for serving ads to digital video players. If your video player can accept the VAST template, then it can play any ad that follows the protocol, thus reducing expensive technical barriers and encouraging advertisers to increase their video ad spend. VAST Calls from Video Player: Normally the video player makes an ad request to the ad server, and the ad server directly serves the VAST response. Within the VAST response, there are various elements for Ad impressions and tracking events. The video player understands these events in a standardized way and reports such events back to the ad server. Now let’s look at another scenario; the above scenario involves only one ad server but the fact remains that there can be multiple ad servers involved in serving the video ad. In this case, the video player first interacts with the primary server, gets a wrapper response and then hits one or more secondary servers to get the actual VAST XML. Since all the servers here will be interested in receiving tracking information, the video player pings each of them with tracking requests. The diagram below illustrates how multiple ad servers serve Video Ads on the web pages. Here are the sample elements in a VAST wrapper XML: <VAST> <Ad> <Wrapper> … <VASTAdTagURI> </VASTAdTagURI> …</Wrapper> </Ad> </VAST> Different Types of Ads supported by VAST: Linear Ads are the ones that you see before the content video begins (pre-roll), in the middle of the video (mid-roll) and at the end (post-roll). Usually in YouTube ads, you would see that the user is given an option to skip an advertisement after a few seconds. With 3.0 VAST Protocol, support for the ‘skip offset attribute’ (usually the value is 00:00:05), skip event and progress event has been introduced. Master or Companion Ads provide a more engaging experience for the user. A Companion Ad is displayed at a specific location of the same web-page while the Master Ad is being played in the video player. Non-Linear Ads are the ads overlaid on the top of video content. Using the VPAID technology, a more engaging user experience can be built, where the main video will get paused when user engages with a Video Ad and resumes when user cancels the ad. The IAB has also come up with new Ad Units known as “Rising Stars”. Rising Stars have been a result of sustained efforts towards greater creativity in interactive advertising, and it encourages rich user engagement. More information on Video Rising Stars is outlined here. With VAST 3.0, support for Ad Pods has been introduced. In this case, a series of ads can be played like TV commercials. This is accomplished by assigning a “sequence” attribute to the Ad element within a VAST 3.0 XML. The ads in an Ad Pod need to be linear but the last ad can have a non-linear creative. The placement an Ad Pod within a Video is outside the scope of VAST 3.0, but this can be accomplished by using VMAP where ad breaks can be specified within the content timeline by the content owners. You can read more about VMAP at iab.net/vsuite/vmap.
Conditional Orders: A Must-have to Tame the Bull.
Being a trader who trades in the derivatives segment of the market, I try and do a lot of my analysis overnight. Before the market opens the next morning, I place triggers for my trade entries & exits. The idea of automating my orders works very well for me as I am working as a Business Analyst (BA) in a major IT firm. Over the past few months, due to a sudden increase in my work assignments, this arrangement has posed a few obstacles for me. Trading has taken a backseat as I am unable to monitor the market regularly and modify my triggers whenever it is needed. Many times my ‘trigger orders’ suffer due to noises in the market, low liquidity or large spreads. Hence I have paused my trading on days of high workload in my job. I had an opportunity to work as a BA on a conditional order system recently, that we were developing for a leading financial corporation in the US. While working on it, I could envision the value that the system could provide to retail traders like me. One of the features of the system enables traders to place trigger orders in option contracts based on the underlier’ s price. Since the triggers are placed on the underlier, problems of large spreads and low liquidity are being tackled effortlessly. Additionally, on important macroeconomic event days, another feature of the system – contingent orders; helps in placing all my orders based on the index’s reaction to the event. The conditional order system developed has Trailing Stop order, Bracketed order, One Triggers Other order, One Cancels Other order and Contingent order. During my initial days of trading as a day trader, I would have loved to have a trailing stop loss, which would have been a blessing for any intraday trader to lock his profits whenever realized and stop out in the case of adverse market event. The bracketed order would be ideal for a trader, who trades based on risk-reward ratios in any trade. A trader can define his exit on the profit side and also have a stop loss in case of a loss at the pre-order stage. He can also blend it with trailing stop-loss orders to place an advanced form of bracketed order by replacing regular stop loss order with trailing stop loss. One Triggers Other order and One Cancels Other orders take a trader a step closer to algo trading. These orders can be used in multiple areas like risk mitigation, margin adherence and locking profits. It’s one of the aspects to the system that I would love to experiment and fine-tune my existing strategies. The conditional order system is definitely a value add to any trader who trades in multiple stocks or commodities or any other asset and for a trader, whose primary job is not trading. With such a system being in place, a lot of problems of traders like me will be resolved. It will also help part time traders to focus on their primary jobs without having to sacrifice trading. With many brokerages in the US already embracing the idea, I have to admit that I have been eagerly waiting for my broker to implement the system.
The Advent of the Third Screen, or May I Wear My Watch Again?
I wear a watch. That fact alone places me in an older demographic since many teens and twenty-somethings have long relied on cell phones as their sole time-keeping device. As a consequence, many watchmakers have suffered in recent years. I said I wear a watch, but it’s actually been out-of-order for several weeks forcing me to reach into my pocket to tell time. The experience has been frustrating and has made me appreciate anew the value of having a watch that I can glance at easily. To each his own, I suppose. There have been attempts to make a smarter watch. Casio Databank watches were the rage in the 80s. They could store phone numbers and reminders, but they couldn’t be connected to an external computer. Twenty years ago, the Timex Datalink allowed the transfer of phone numbers, appointments, anniversaries, to-do lists, reminders, and alarms from a PC to the watch encoded in the CRT’s emitted light. Ten years later, Microsoft introduced the SPOT Watch which could receive email, weather forecast, stocks info, and news via FM radio bands. The SPOT could be updated dynamically if the user was in range of a compatible FM signal. In the end, these devices didn’t last and were superseded by Palm PDAs, and, later, smart phones. There has been a lot of buzz lately about the possible resurgence of watches. There are persistent rumors about an Apple iWatch that could potentially make watches cooler again. Other possible makers of smart watches include Samsung, LG, and Microsoft. These new smart watches would interact with a cell phone via Bluetooth, and, possibly, via Wi-Fi and cellular networks. The latter is less likely because of the power requirements of a cellular connection. There are a number of smart watches already on the market including the Pebble which uses Bluetooth to connect to an iPhone or Android phone and displays the time, email headers, reminders and text message. Consider the nascent field of the second or companion screen. The idea is that your main screen (usually the TV or the computer monitor) can be augmented by having an app running on a smart phone or a tablet. What if in the near future, most computing was performed on mobile devices, which would then be augmented by smaller devices such as smart watches. In essence, a smart watch would be a third screen; a companion to a second screen mobile device. Such a pairing would redefine the client-server concept. This is more than just a story about watches. A whole range of products could act as a third screen including: Head-mounted display systems such as Google Glass. A car HUD (Head-up display) A smart refrigerator with a display panel, etc… You could imagine scenarios where the computing devices around you would be aware of your presence via Bluetooth, Wi-Fi, NFC, etc…, and automatically become third screens to your cell phone. You might be sitting in front of a TV which would display information about an incoming call. A similar situation could occur if you sat down in front of a PC, even one that didn’t belong to you. In a role-reversal, these main screens would become third screens to your second-screen mobile devices. With the definition of a common interface, any device you encounter could become your display of choice at that moment. A bit far out? Perhaps. Nonetheless, third screens are coming, potentially opening up a whole new field for software developers. Now, may I wear my watch again?
Tested Tips for Successful eCommerce Testing
The history of online retail is littered with expensive failures, many of which could have been avoided by better testing before the site was opened to the customers. Customers are unlikely to have confidence in a website that goes through frequent downtimes, hangs during a transaction, or has usability issues. All these reasons make testing crucial for eCommerce environment and any failure can be expensive in terms of lost revenue and dissatisfied customers seeking alternative sites. Most software projects operate within tight budgets and timelines, so QA managers need a systematic and cost-effective approach to testing that maximizes test confidence. Below are the areas to focus on in eCommerce testing: 1. Browser Compatibility Remember this as a thumb rule: your application must perform consistently at-least in the top 3 browsers. 2. Browser Tones Testing should cover the main platforms (UNIX/Linux, Windows, Mac) and the expected language options. 3. Page Appearance The appearance of web pages in a browser forms the all-important interface between the buyer and the business. You can’t afford to get this one wrong. 4. Runtime Error Messages Consumers get frustrated when a browser throws up gibberish. Ensure that the application captures and handles all errors by generating an apt and user-friendly error page. 5. Numb/Broken hyperlinks. You don’t want links on your Website that lead to… nowhere. Try out automated tools such as Xenu and LinkChecker or websites such as brokenlinkcheck.com 6. Page download times. Many studies estimate that page load times of ten seconds or more, combined with ISP download times could cause up to 33% of customers to leave a site before they buy anything. Test download time under genuine test conditions, rather than testing it locally. 7. Transactions Transaction processing is a dominant element of eCommerce applications. Test integrity and security of transactions. 8. Shopping, Order processing, and Purchasing In my experience, functional testing consumes between 30% and 50% of the total testing effort. In most eCommerce systems, shopping and order processing form the core functionality. Although most engineers largely consider functional testing a manual process, tools (such as QTP and Selenium) can often help automate aspects of functional testing by automatically capturing and re-running user interactions. 9. Tax and shipping calculations You might have to handle multiple taxes and shipping rates. The problem becomes more interesting if you have customers outside the country. Testing is necessary to ensure that the customer is charged the correct tax and shipping amount. 10. Security Security (or a lack of it) is a barrier to eCommerce. With the rise in credit card scams and high-profile hackings, buyers avoid websites they perceive to be insecure. Penetration testing is necessary to find out vulnerabilities before anyone else can. We have looked at areas to focus on in eCommerce testing for the delivery and presentation of content: But the question is … where should we initiate testing? I will share additional insights on it in my next blog… stay tuned!
What’s so Big about Big Data?
About 90% of the data in today’s World has been created only in the last two years. As per Google, every 2 days we create as much information as we did till 2003. There are around 200 million Tweets every day. Facebook gets around 6 billion messages per day. When it comes to handling this kind of a data explosion, conventional RDBMS has its own limitations; that’s where Big Data comes into the picture. Big Data is about handling the 3Vs – Volume, Velocity, and Variety. Volume: Big Data can handle data in Petabytes or more; this is a difficult task in RDBMS. Velocity: Velocity describes the frequency at which data is generated, captured and shared. Big Data is capable of handling dynamic data from diverse sources—online systems, sensors, social media, web clickstream, and other channels. Variety: Big Data comprises all types of data—structured, semi-structured and unstructured data (such as text, sensor data, audio, video, click streams, log files and more). According to O’Reilly Media,., “Big data is data that exceeds the processing capacity of conventional database systems. The data is too big, moves too fast, or doesn’t fit the structures of your database architectures. To gain value from this data, you must choose an alternative way to process it.” The comparison chart shown below throws more light on the differences between RDBMS and Big Data. RDBMS Big Data Variety Places data inside well-defined structures or tables using meta data. But it can’t handle semi-structured and unstructured data—like photos, videos and posting messages on Social Media. Has the capability to handle a variety of data (structured, semi-structured and unstructured data) through different NoSQL databases like graph, document, key-value and column family databases. Volume Can handle data in MBs and GBs better than any Big Data system, but its performance goes down as the data size increases to TBs or PBs. The RDBMS system can be scaled up and not scaled out. Also, the cost of scaling up a system is high. Good in handling a large size of data. So it is very efficiently used by sites like Facebook, LinkedIn and Twitter, where the data size is huge. Big Data handles this task through scaling out on commodity hardware. Velocity Can handle small sets of data, but can’t manage the speed at which data arrives on sites like Facebook, Twitter, etc. So, the performance will be poor when the velocity is high. Can easily handle high velocity data like the millions/billions of messages arriving on social networking sites. It can handle the data through parallel processing, which is not possible in RDBMS. Apart from data storage and retrieval, Big Data has capabilities to process the data efficiently, e.g., we can divide the data to be processed into hundreds or thousands of commodity hardware, and the data can be processed independently on each machine. So the bottom line is that the omnipresent and ongoing buzz around Big Data is definitely not a passing fad. This claim can be substantiated with the fact that today’s technology-driven, net-enabled businesses are continuing to count on Big Data – big time!
Social TV: You’re not “Home Alone” Anymore!
There has been a lot of buzz and hype surrounding Social TV. But, TV was always social—wasn’t it? Remember the days when our family sat in front of the TV to watch a weekend movie or a Super Bowl? We used to root for our favorite actors and players. It was easier to express emotions and share opinions in real time. Today the TV still exists, but the audience is scattered. The same movies are shown on TV, and the Super Bowl is still being telecast right in the living room but, the viewing experience has changed! With the advent of Internet—and more so with the likes of Facebook and Twitter—the word “Social” has become much in ‘vogue’. And if you thought the internet has killed TV, there’s some good news for you – both have become very good friends. More and more people in America are watching television than ever before, and engaging in online activities at the same time. They update Facebook and chat about the program they are watching. Well, this is called “Social TV”. Are you not socializing while watching TV? So the essence of socializing is still the same; only the medium has changed. Social TV refers to the technologies, surrounding television, that promote communication and social interaction related to program content. Social TV can leverage diverse technologies—like text chat, voice communication, TV recommendations, ratings, context awareness, etc.—so that users can share, view and experience the same show, movie or game on TV with their friends. Social TV is also an opportunity for content producers and TV operators to offer new services and increase revenue by studying the consumers’ TV-related social behavior, devices and networks. The networks would have an improved ability to understand broader audience engagement and affinity for TV shows. In turn, this data could be harnessed to drive greater tune-ins, boost viewer loyalty, optimize marketing promotions, and increase ad revenue. On the other hand, advertisers could better evaluate the value created by social media around their commercial placements or product integrations. These findings could be used to gather information on the shows or genres that drive more social conversations and create a buzz about a brand. This in turn would help in improving the return on traditional ad buys. Social TV has actually created the need for greater user interactivity without going to an external source. This need has led to the emergence of the Second Screen—a term that refers to an additional electronic device (e.g. tablet, smartphone) that allows a television audience to interact with the content they are consuming.
Software Testing: To automate or not, that is the question
We all understand the importance and need of test automation. The first and most important step to successful automation is to determine what to automate (first). Which Test Cases to Automate? The ROI of automated testing is usually correlated with how many times a given test case will be repeated. Tests that are only performed a few times, are better left for manual testing. Good test cases for automation are those that are run frequently and require large amounts of data to perform the same action. Testers can get the most out of their testing efforts by automating: Test cases that should be re-executed in each new build or release (usually regression) Tests that are subject to human error Tests that require multiple data sets Frequently-used functionality that introduces high risk conditions Tests that run on several different hardware or software platforms and configurations High risk, business critical test cases Test cases that are very tedious or difficult to perform manually The following categories of test cases are not suitable for automation: Test cases that are newly designed and not executed manually even once Test cases for which the requirements are changing frequently Test cases that are executed on an ad-hoc basis Success in test automation requires careful planning and design, apart from a robust framework for a given test scope. In my next blog post, I will discuss some of the best practices of test automation. Stay tuned…