Wednesday, July 2, 2008

Software Testing FAQ

What are 5 common problems in the software development process?

  • poor requirements - if requirements are unclear, incomplete, too general, and not testable, there will be problems.
  • unrealistic schedule - if too much work is crammed in too little time, problems are inevitable.
  • inadequate testing - no one will know whether or not the program is any good until the customer complains or systems crash.
  • featuritis - requests to pile on new features after development is underway; extremely common.
  • miscommunication - if developers don’t know what’s needed or customer’s have erroneous expectations, problems can be expected.

What are 5 common solutions to software development problems?

  • solid requirements - clear, complete, detailed, cohesive, attainable, testable requirements that are agreed to by all players. In ‘agile’-type environments, continuous close coordination with customers/end-users is necessary to ensure that changing/emerging requirements are understood.
  • realistic schedules - allow adequate time for planning, design, testing, bug fixing, re-testing, changes, and documentation; personnel should be able to complete the project without burning out.
  • adequate testing - start testing early on, re-test after fixes or changes, plan for adequate time for testing and bug-fixing. ‘Early’ testing could include static code analysis/testing, test-first development, unit testing by developers, built-in testing and diagnostic capabilities, automated post-build testing, etc.
  • stick to initial requirements where feasible - be prepared to defend against excessive changes and additions once development has begun, and be prepared to explain consequences. If changes are necessary, they should be adequately reflected in related schedule changes. If possible, work closely with customers/end-users to manage expectations. In ‘agile’-type environments, initial requirements may be expected to change significantly, requiring that true agile processes be in place.
  • communication - require walkthroughs and inspections when appropriate; make extensive use of group communication tools - groupware, wiki’s, bug-tracking tools and change management tools, intranet capabilities, etc.; ensure that information/documentation is available and up-to-date - preferably electronic, not paper; promote teamwork and cooperation; use protoypes and/or continuous communication with end-users if possible to clarify expectations.

What are some recent major computer system failures caused by software bugs?

  • News reports in December of 2007 indicated that significant software problems were continuing to occur in a new ERP payroll system for a large urban school system. It was believed that more than one third of employees had received incorrect paychecks at various times since the new system went live in January of that year, resulting in overpayments of $53 million, as well as underpayments. An employees’ union brought a lawsuit against the school system, the cost of the ERP system was expected to rise by 40%, and the non-payroll part of the ERP system was delayed. Inadequate testing reportedly contributed to the problems.
  • In November of 2007 a regional government reportedly brought a multi-million dollar lawsuit against a software services vendor, claiming that the vendor ‘minimized quality’ in delivering software for a large criminal justice information system and the system did not meet requirements. The vendor also sued its subcontractor on the project.
  • In June of 2007 news reports claimed that software flaws in a popular online stock-picking contest could be used to gain an unfair advantage in pursuit of the game’s large cash prizes. Outside investigators were called in and in July the contest winner was announced. Reportedly the winner had previously been in 6th place, indicating that the top 5 contestants may have been disqualified.
  • A software problem contributed to a rail car fire in a major underground metro system in April of 2007 according to newspaper accounts. The software reportedly failed to perform as expected in detecting and preventing excess power usage in equipment on a new passenger rail car, resulting in overheating and fire in the rail car, and evacuation and shutdown of part of the system.
  • Tens of thousands of medical devices were recalled in March of 2007 to correct a software bug. According to news reports, the software would not reliably indicate when available power to the device was too low.
  • A September 2006 news report indicated problems with software utilized in a state government’s primary election, resulting in periodic unexpected rebooting of voter checkin machines, which were separate from the electronic voting machines, and resulted in confusion and delays at voting sites. The problem was reportedly due to insufficient testing.
  • In August of 2006 a U.S. government student loan service erroneously made public the personal data of as many as 21,000 borrowers on it’s web site, due to a software error. The bug was fixed and the government department subsequently offered to arrange for free credit monitoring services for those affected.
  • A software error reportedly resulted in overbilling of up to several thousand dollars to each of 11,000 customers of a major telecommunications company in June of 2006. It was reported that the software bug was fixed within days, but that correcting the billing errors would take much longer.
  • News reports in May of 2006 described a multi-million dollar lawsuit settlement paid by a healthcare software vendor to one of its customers. It was reported that the customer claimed there were problems with the software they had contracted for, including poor integration of software modules, and problems that resulted in missing or incorrect data used by medical personnel.
  • In early 2006 problems in a government’s financial monitoring software resulted in incorrect election candidate financial reports being made available to the public. The government’s election finance reporting web site had to be shut down until the software was repaired.
  • Trading on a major Asian stock exchange was brought to a halt in November of 2005, reportedly due to an error in a system software upgrade. The problem was rectified and trading resumed later the same day.
  • A May 2005 newspaper article reported that a major hybrid car manufacturer had to install a software fix on 20,000 vehicles due to problems with invalid engine warning lights and occasional stalling. In the article, an automotive software specialist indicated that the automobile industry spends $2 billion to $3 billion per year fixing software problems.
  • Media reports in January of 2005 detailed severe problems with a $170 million high-profile U.S. government IT systems project. Software testing was one of the five major problem areas according to a report of the commission reviewing the project. In March of 2005 it was decided to scrap the entire project.
  • In July 2004 newspapers reported that a new government welfare management system in Canada costing several hundred million dollars was unable to handle a simple benefits rate increase after being put into live operation. Reportedly the original contract allowed for only 6 weeks of acceptance testing and the system was never tested for its ability to handle a rate increase.
  • Millions of bank accounts were impacted by errors due to installation of inadequately tested software code in the transaction processing system of a major North American bank, according to mid-2004 news reports. Articles about the incident stated that it took two weeks to fix all the resulting errors, that additional problems resulted when the incident drew a large number of e-mail phishing attacks against the bank’s customers, and that the total cost of the incident could exceed $100 million.
  • A bug in site management software utilized by companies with a significant percentage of worldwide web traffic was reported in May of 2004. The bug resulted in performance problems for many of the sites simultaneously and required disabling of the software until the bug was fixed.
  • According to news reports in April of 2004, a software bug was determined to be a major contributor to the 2003 Northeast blackout, the worst power system failure in North American history. The failure involved loss of electrical power to 50 million customers, forced shutdown of 100 power plants, and economic losses estimated at $6 billion. The bug was reportedly in one utility company’s vendor-supplied power monitoring and management system, which was unable to correctly handle and report on an unusual confluence of initially localized events. The error was found and corrected after examining millions of lines of code.
  • In early 2004, news reports revealed the intentional use of a software bug as a counter-espionage tool. According to the report, in the early 1980’s one nation surreptitiously allowed a hostile nation’s espionage service to steal a version of sophisticated industrial software that had intentionally-added flaws. This eventually resulted in major industrial disruption in the country that used the stolen flawed software.
  • A major U.S. retailer was reportedly hit with a large government fine in October of 2003 due to web site errors that enabled customers to view one anothers’ online orders.
  • News stories in the fall of 2003 stated that a manufacturing company recalled all their transportation products in order to fix a software problem causing instability in certain circumstances. The company found and reported the bug itself and initiated the recall procedure in which a software upgrade fixed the problems.
  • In August of 2003 a U.S. court ruled that a lawsuit against a large online brokerage company could proceed; the lawsuit reportedly involved claims that the company was not fixing system problems that sometimes resulted in failed stock trades, based on the experiences of 4 plaintiffs during an 8-month period. A previous lower court’s ruling that “…six miscues out of more than 400 trades does not indicate negligence.” was invalidated.
  • In April of 2003 it was announced that a large student loan company in the U.S. made a software error in calculating the monthly payments on 800,000 loans. Although borrowers were to be notified of an increase in their required payments, the company will still reportedly lose $8 million in interest. The error was uncovered when borrowers began reporting inconsistencies in their bills.
  • News reports in February of 2003 revealed that the U.S. Treasury Department mailed 50,000 Social Security checks without any beneficiary names. A spokesperson indicated that the missing names were due to an error in a software change. Replacement checks were subsequently mailed out with the problem corrected, and recipients were then able to cash their Social Security checks.
  • In March of 2002 it was reported that software bugs in Britain’s national tax system resulted in more than 100,000 erroneous tax overcharges. The problem was partly attributed to the difficulty of testing the integration of multiple systems.
  • A newspaper columnist reported in July 2001 that a serious flaw was found in off-the-shelf software that had long been used in systems for tracking certain U.S. nuclear materials. The same software had been recently donated to another country to be used in tracking their own nuclear materials, and it was not until scientists in that country discovered the problem, and shared the information, that U.S. officials became aware of the problems.
  • According to newspaper stories in mid-2001, a major systems development contractor was fired and sued over problems with a large retirement plan management system. According to the reports, the client claimed that system deliveries were late, the software had excessive defects, and it caused other systems to crash.
  • In January of 2001 newspapers reported that a major European railroad was hit by the aftereffects of the Y2K bug. The company found that many of their newer trains would not run due to their inability to recognize the date ‘31/12/2000′; the trains were started by altering the control system’s date settings.
  • News reports in September of 2000 told of a software vendor settling a lawsuit with a large mortgage lender; the vendor had reportedly delivered an online mortgage processing system that did not meet specifications, was delivered late, and didn’t work.
  • In early 2000, major problems were reported with a new computer system in a large suburban U.S. public school district with 100,000+ students; problems included 10,000 erroneous report cards and students left stranded by failed class registration systems; the district’s CIO was fired. The school district decided to reinstate it’s original 25-year old system for at least a year until the bugs were worked out of the new system by the software vendors.
  • A review board concluded that the NASA Mars Polar Lander failed in December 1999 due to software problems that caused improper functioning of retro rockets utilized by the Lander as it entered the Martian atmosphere.
  • In October of 1999 the $125 million NASA Mars Climate Orbiter spacecraft was believed to be lost in space due to a simple data conversion error. It was determined that spacecraft software used certain data in English units that should have been in metric units. Among other tasks, the orbiter was to serve as a communications relay for the Mars Polar Lander mission, which failed for unknown reasons in December 1999. Several investigating panels were convened to determine the process failures that allowed the error to go undetected.
  • Bugs in software supporting a large commercial high-speed data network affected 70,000 business customers over a period of 8 days in August of 1999. Among those affected was the electronic trading system of the largest U.S. futures exchange, which was shut down for most of a week as a result of the outages.
  • In April of 1999 a software bug caused the failure of a $1.2 billion U.S. military satellite launch, the costliest unmanned accident in the history of Cape Canaveral launches. The failure was the latest in a string of launch failures, triggering a complete military and industry review of U.S. space launch programs, including software integration and testing processes. Congressional oversight hearings were requested.
  • A small town in Illinois in the U.S. received an unusually large monthly electric bill of $7 million in March of 1999. This was about 700 times larger than its normal bill. It turned out to be due to bugs in new software that had been purchased by the local power company to deal with Y2K software issues.
  • In early 1999 a major computer game company recalled all copies of a popular new product due to software problems. The company made a public apology for releasing a product before it was ready.
  • The computer system of a major online U.S. stock trading service failed during trading hours several times over a period of days in February of 1999 according to nationwide news reports. The problem was reportedly due to bugs in a software upgrade intended to speed online trade confirmations.
  • In April of 1998 a major U.S. data communications network failed for 24 hours, crippling a large part of some U.S. credit card transaction authorization systems as well as other large U.S. bank, retail, and government data systems. The cause was eventually traced to a software bug.
  • January 1998 news reports told of software problems at a major U.S. telecommunications company that resulted in no charges for long distance calls for a month for 400,000 customers. The problem went undetected until customers called up with questions about their bills.
  • In November of 1997 the stock of a major health industry company dropped 60% due to reports of failures in computer billing systems, problems with a large database conversion, and inadequate software testing. It was reported that more than $100,000,000 in receivables had to be written off and that multi-million dollar fines were levied on the company by government agencies.
  • A retail store chain filed suit in August of 1997 against a transaction processing system vendor (not a credit card company) due to the software’s inability to handle credit cards with year 2000 expiration dates.
  • In August of 1997 one of the leading consumer credit reporting companies reportedly shut down their new public web site after less than two days of operation due to software problems. The new site allowed web site visitors instant access, for a small fee, to their personal credit reports. However, a number of initial users ended up viewing each others’ reports instead of their own, resulting in irate customers and nationwide publicity. The problem was attributed to “…unexpectedly high demand from consumers and faulty software that routed the files to the wrong computers.”
  • In November of 1996, newspapers reported that software bugs caused the 411 telephone information system of one of the U.S. RBOC’s to fail for most of a day. Most of the 2000 operators had to search through phone books instead of using their 13,000,000-listing database. The bugs were introduced by new software modifications and the problem software had been installed on both the production and backup systems. A spokesman for the software vendor reportedly stated that ‘It had nothing to do with the integrity of the software. It was human error.’
  • On June 4 1996 the first flight of the European Space Agency’s new Ariane 5 rocket failed shortly after launching, resulting in an estimated uninsured loss of a half billion dollars. It was reportedly due to the lack of exception handling of a floating-point error in a conversion from a 64-bit integer to a 16-bit signed integer.
  • Software bugs caused the bank accounts of 823 customers of a major U.S. bank to be credited with $924,844,208.32 each in May of 1996, according to newspaper reports. The American Bankers Association claimed it was the largest such error in banking history. A bank spokesman said the programming errors were corrected and all funds were recovered.
  • On January 1 1984 all computers produced by one of the leading minicomputer makers of the time reportedly failed worldwide. The cause was claimed to be a leap year bug in a date handling function utilized in deletion of temporary operating system files. Technicians throughout the world worked for several days to clear up the problem. It was also reported that the same bug affected many of the same computers four years later.
  • Software bugs in a Soviet early-warning monitoring system nearly brought on nuclear war in 1983, according to news reports in early 1999. The software was supposed to filter out false missile detections caused by Soviet satellites picking up sunlight reflections off cloud-tops, but failed to do so. Disaster was averted when a Soviet commander, based on what he said was a ‘…funny feeling in my gut’, decided the apparent missile attack was a false alarm. The filtering software code was rewritten.

What kinds of testing should be considered?

  • Black box testing - not based on any knowledge of internal design or code. Tests are based on requirements and functionality.
  • White box testing - based on knowledge of the internal logic of an application’s code. Tests are based on coverage of code statements, branches, paths, conditions.
  • unit testing - the most ‘micro’ scale of testing; to test particular functions or code modules. Typically done by the programmer and not by testers, as it requires detailed knowledge of the internal program design and code. Not always easily done unless the application has a well-designed architecture with tight code; may require developing test driver modules or test harnesses.
  • incremental integration testing - continuous testing of an application as new functionality is added; requires that various aspects of an application’s functionality be independent enough to work separately before all parts of the program are completed, or that test drivers be developed as needed; done by programmers or by testers.
  • integration testing - testing of combined parts of an application to determine if they function together correctly. The ‘parts’ can be code modules, individual applications, client and server applications on a network, etc. This type of testing is especially relevant to client/server and distributed systems.
  • functional testing - black-box type testing geared to functional requirements of an application; this type of testing should be done by testers. This doesn’t mean that the programmers shouldn’t check that their code works before releasing it (which of course applies to any stage of testing.)
  • system testing - black-box type testing that is based on overall requirements specifications; covers all combined parts of a system.
  • end-to-end testing - similar to system testing; the ‘macro’ end of the test scale; involves testing of a complete application environment in a situation that mimics real-world use, such as interacting with a database, using network communications, or interacting with other hardware, applications, or systems if appropriate.
  • sanity testing or smoke testing - typically an initial testing effort to determine if a new software version is performing well enough to accept it for a major testing effort. For example, if the new software is crashing systems every 5 minutes, bogging down systems to a crawl, or corrupting databases, the software may not be in a ’sane’ enough condition to warrant further testing in its current state.
  • regression testing - re-testing after fixes or modifications of the software or its environment. It can be difficult to determine how much re-testing is needed, especially near the end of the development cycle. Automated testing tools can be especially useful for this type of testing.
  • acceptance testing - final testing based on specifications of the end-user or customer, or based on use by end-users/customers over some limited period of time.
  • load testing - testing an application under heavy loads, such as testing of a web site under a range of loads to determine at what point the system’s response time degrades or fails.
  • stress testing - term often used interchangeably with ‘load’ and ‘performance’ testing. Also used to describe such tests as system functional testing while under unusually heavy loads, heavy repetition of certain actions or inputs, input of large numerical values, large complex queries to a database system, etc.
  • performance testing - term often used interchangeably with ’stress’ and ‘load’ testing. Ideally ‘performance’ testing (and any other ‘type’ of testing) is defined in requirements documentation or QA or Test Plans.
  • usability testing - testing for ‘user-friendliness’. Clearly this is subjective, and will depend on the targeted end-user or customer. User interviews, surveys, video recording of user sessions, and other techniques can be used. Programmers and testers are usually not appropriate as usability testers.
  • install/uninstall testing - testing of full, partial, or upgrade install/uninstall processes.
  • recovery testing - testing how well a system recovers from crashes, hardware failures, or other catastrophic problems.
  • failover testing - typically used interchangeably with ‘recovery testing’
  • security testing - testing how well the system protects against unauthorized internal or external access, willful damage, etc; may require sophisticated testing techniques.
  • compatability testing - testing how well software performs in a particular hardware/software/operating system/network/etc. environment.
  • exploratory testing - often taken to mean a creative, informal software test that is not based on formal test plans or test cases; testers may be learning the software as they test it.
  • ad-hoc testing - similar to exploratory testing, but often taken to mean that the testers have significant understanding of the software before testing it.
  • context-driven testing - testing driven by an understanding of the environment, culture, and intended use of software. For example, the testing approach for life-critical medical equipment software would be completely different than that for a low-cost computer game.
  • user acceptance testing - determining if software is satisfactory to an end-user or customer.
  • comparison testing - comparing software weaknesses and strengths to competing products.
  • alpha testing - testing of an application when development is nearing completion; minor design changes may still be made as a result of such testing. Typically done by end-users or others, not by programmers or testers.
  • beta testing - testing when development and testing are essentially completed and final bugs and problems need to be found before final release. Typically done by end-users or others, not by programmers or testers.
  • mutation testing - a method for determining if a set of test data or test cases is useful, by deliberately introducing various code changes (’bugs’) and retesting with the original test data/cases to determine if the ‘bugs’ are detected. Proper implementation requires large computational resources.

Will automated testing tools make testing easier?

  • Possibly. For small projects, the time needed to learn and implement them may not be worth it unless personnel are already familiar with the tools. For larger projects, or on-going long-term projects they can be valuable.
  • A common type of automated tool is the ‘record/playback’ type. For example, a tester could click through all combinations of menu choices, dialog box choices, buttons, etc. in an application GUI and have them ‘recorded’ and the results logged by a tool. The ‘recording’ is typically in the form of text based on a scripting language that is interpretable by the testing tool. Often the recorded script is manually modified and enhanced. If new buttons are added, or some underlying code in the application is changed, etc. the application might then be retested by just ‘playing back’ the ‘recorded’ actions, and comparing the logging results to check effects of the changes. The problem with such tools is that if there are continual changes to the system being tested, the ‘recordings’ may have to be changed so much that it becomes very time-consuming to continuously update the scripts. Additionally, interpretation and analysis of results (screens, data, logs, etc.) can be a difficult task. Note that there are record/playback tools for text-based interfaces also, and for all types of platforms.
  • Another common type of approach for automation of functional testing is ‘data-driven’ or ‘keyword-driven’ automated testing, in which the test drivers are separated from the data and/or actions utilized in testing (an ‘action’ would be something like ‘enter a value in a text box’). Test drivers can be in the form of automated test tools or custom-written testing software. The data and actions can be more easily maintained - such as via a spreadsheet - since they are separate from the test drivers. The test drivers ‘read’ the data/action information to perform specified tests. This approach can enable more efficient control, development, documentation, and maintenance of automated tests/test cases.
  • Other automated tools can include:

code analyzers - monitor code complexity, adherence to standards, etc.

coverage analyzers - these tools check which parts of the code have been exercised by a test, and may be oriented to code statement coverage, condition coverage, path coverage, etc.

memory analyzers - such as bounds-checkers and leak detectors.

load/performance test tools - for testing client/server and web applications under various load levels.

web test tools - to check that links are valid, HTML code usage is correct, client-side and
server-side programs work, a web site’s interactions are secure.

other tools - for test case management, documentation management, bug reporting, and configuration management, file and database comparisons, screen captures, security testing, macro recorders, etc.

Test automation is, of course, possible without COTS tools. Many successful automation efforts utilize custom automation software that is targeted for specific projects, specific software applications, or a specific organization’s software development environment. In test-driven agile software development environments, automated tests are often built into the software during (or preceding) coding of the application.

What steps are needed to develop and run software tests?
The following are some of the steps to consider:

  • Obtain requirements, functional design, and internal design specifications and other necessary documents
  • Obtain budget and schedule requirements
  • Determine project-related personnel and their responsibilities, reporting requirements, required standards and processes (such as release processes, change processes, etc.)
  • Determine project context, relative to the existing quality culture of the organization and business, and how it might impact testing scope, aproaches, and methods.
  • Identify application’s higher-risk aspects, set priorities, and determine scope and limitations of tests
  • Determine test approaches and methods - unit, integration, functional, system, load, usability tests, etc.
  • Determine test environment requirements (hardware, software, communications, etc.)
  • Determine testware requirements (record/playback tools, coverage analyzers, test tracking, problem/bug tracking, etc.)
  • Determine test input data requirements
  • Identify tasks, those responsible for tasks, and labor requirements
  • Set schedule estimates, timelines, milestones
  • Determine input equivalence classes, boundary value analyses, error classes
  • Prepare test plan document and have needed reviews/approvals
  • Write test cases
  • Have needed reviews/inspections/approvals of test cases
  • Prepare test environment and testware, obtain needed user manuals/reference documents/configuration guides/installation guides, set up test tracking processes, set up logging and archiving processes, set up or obtain test input data
  • Obtain and install software releases
  • Perform tests
  • Evaluate and report results
  • Track problems/bugs and fixes
  • Retest as needed
  • Maintain and update test plans, test cases, test environment, and testware through life cycle

What is ‘good code’ in project?
‘Good code’ is code that works, is reasonably bug free, and is readable and maintainable. Some organizations have coding ’standards’ that all developers are supposed to adhere to, but everyone has different ideas about what’s best, or what is too many or too few rules. There are also various theories and metrics, such as McCabe Complexity metrics. It should be kept in mind that excessive use of standards and rules can stifle productivity and creativity. ‘Peer reviews’, ‘buddy checks’ pair programming, code analysis tools, etc. can be used to check for problems and enforce standards.
For C and C++ coding, here are some typical ideas to consider in setting rules/standards; these may or may not apply to a particular situation:

  • minimize or eliminate use of global variables.
  • use descriptive function and method names - use both upper and lower case, avoid abbreviations, use as many characters as necessary to be adequately descriptive (use of more than 20 characters is not out of line); be consistent in naming conventions.
  • use descriptive variable names - use both upper and lower case, avoid abbreviations, use as many characters as necessary to be adequately descriptive (use of more than 20 characters is not out of line); be consistent in naming conventions.
  • function and method sizes should be minimized; less than 100 lines of code is good, less than 50 lines is preferable.
  • function descriptions should be clearly spelled out in comments preceding a function’s code.
  • organize code for readability.
  • use whitespace generously - vertically and horizontally
  • each line of code should contain 70 characters max.
  • one code statement per line.
  • coding style should be consistent throught a program (eg, use of brackets, indentations, naming conventions, etc.)
  • in adding comments, err on the side of too many rather than too few comments; a common rule of thumb is that there should be at least as many lines of comments (including header blocks) as lines of code.
  • no matter how small, an application should include documentaion of the overall program function and flow (even a few paragraphs is better than nothing); or if possible a separate flow chart and detailed program documentation.
  • make extensive use of error handling procedures and status and error logging.
  • for C++, to minimize complexity and increase maintainability, avoid too many levels of inheritance in class heirarchies (relative to the size and complexity of the application). Minimize use of multiple inheritance, and minimize use of operator overloading (note that the Java programming language eliminates multiple inheritance and operator overloading.)
  • for C++, keep class methods small, less than 50 lines of code per method is preferable.
  • for C++, make liberal use of exception handlers

What is a ‘walkthrough’?
A ‘walkthrough’ is an informal meeting for evaluation or informational purposes. Little or no preparation is usually required.

What is verification? validation?
Verification typically involves reviews and meetings to evaluate documents, plans, code, requirements, and specifications. This can be done with checklists, issues lists, walkthroughs, and inspection meetings. Validation typically involves actual testing and takes place after verifications are completed. The term ‘IV & V’ refers to Independent Verification and Validation.

What makes a good Software Test engineer?
A good test engineer has a ‘test to break’ attitude, an ability to take the point of view of the customer, a strong desire for quality, and an attention to detail. Tact and diplomacy are useful in maintaining a cooperative relationship with developers, and an ability to communicate with both technical (developers) and non-technical (customers, management) people is useful. Previous software development experience can be helpful as it provides a deeper understanding of the software development process, gives the tester an appreciation for the developers’ point of view, and reduce the learning curve in automated test tool programming. Judgement skills are needed to assess high-risk areas of an application on which to focus testing efforts when time is limited.


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