Sometimes the structures we generate are test cases themselves, and sometimes they are used to help us design test cases. To use syntax testing we must first describe the valid or acceptable data in a formal notation such as the Backus Naur Form, or BNF for short. Indeed, an important feature of syntax testing is the use of a syntactic description such as BNF or a grammar.
The SPARQL query is parsed into a corresponding algebra tree using Jena ARQ. The equivalent Spark SQL expression is generated based on the ExtVP schema by traversing the tree from bottom up. The equivalent Spark SQL query generated after mapping is executed by Spark. S2RDF optimizes queries using the technique of triple reordering by selectivity estimation. For evaluating the generated SQL query the precomputed semi-join tables can be used by S2RDF if they exist, or it alternatively uses the base encoding tables. SPARQLGX  directly compiles the SPARQL queries into Spark operations.
Returns the TestInfo for the i-th test among all the tests. GoogleTest calls TearDown()
after running each individual test. RecordProperty is public static so it can be called from utility functions
that are not members of the test fixture. The key must be a valid XML attribute name, and cannot conflict with the ones
already used by GoogleTest (name, file, line, status, time,
classname, type_param, and value_param). Performs shared teardown for all tests in the test suite. GoogleTest calls
TearDownTestSuite() after running the last test in the test suite.
Look online for the tool Jester (jester.sourceforge.net), which is based on JUnit. Based on your reading, evaluate Jester as a mutation-testing tool. (Challenging!) Find or write a small SMV specification and a corresponding Java implementation. Mutate the assertions systematically, and collect the traces from (nonequivalent) mutants. Consider how often the idea of covering nodes and edges pops up in software testing. A value-parameterized test fixture class must inherit from both Test
Syntax Testing – Steps:
It uses Jena ARQ to walk through the SPARQL query and generate a SPARQL algebra expression tree. The Spark SQL engine is used to evaluate the created SQL query. The result set obtained of this query is in form of a Spark DataFrame that is further mapped into a SPARQL binding. It applies algebraic optimizations and normalizations like constant folding and filter placement on the algebraic expression. The Jena ARQ engine is used in  for checking syntax and generating algebra tree. The optimization of SPARQL queries based on Pig Latin means reducing the I/O required for transferring data between mappers and reducers as well as the data that is read from and stored into HDFS.
The test suite
must be registered with
REGISTER_TYPED_TEST_SUITE_P. Instantiates the value-parameterized test suite TestSuiteName (defined with
TEST_P). TestFixtureName must be
the name of a test fixture class—see
Test Fixtures. The query parser module in Jiuyun et al.  uses the semantic connection set (SCS) optimization strategy, triple pattern join order and broadcast variable information for generating a query plan. A SCS contains the multiple intermediate results obtained after matching multiple triple patterns which are sorted in an ascending order on the basis of size of its matching results.
Classes and types
TestFixtureName must be
the name of a value-parameterized test fixture class—see
Value-Parameterized Tests. The two join techniques in MapReduce are Reduce side or Repartition join and Map-side join. MAPSIN  overcomes the drawbacks of both these techniques by transferring only necessary data over the network and by using the distributed index of HBase. The join between two triple patterns is computed in a single map phase by using the MAPSIN join technique. In comparison to the reduce-side join approach which transfers lot of data over the network, in the MAPSIN join approach only the data that is really required is transferred. The resultant set of mappings computed finally are stored in HDFS.
Provide reachability conditions, infection conditions, propagation conditions, and test case values to kill mutants 2, 4, 5, and 6 in Figure 9.1. It returns 0 if all tests are
successful, or 1 otherwise. See
Registering tests programmatically
for more information.
How to Perform Syntax Testing?
But again, don’t let me sway your career choices simply because of my bias – go with what is best for you. Both approaches are appropriate and complement each other. Static analysis tools might uncover flaws in code that have not even yet been fully implemented in a way that would expose the flaw to dynamic testing.
- Croft et al. (2010) is a very readable introduction to IR and web search engines.
- Syntax testing is a shotgun method that depends on many test cases.
- As we saw earlier, syntax testing is a special data-driven technique, which was developed as a tool for testing the input data to language processors such as compilers or interpreters.
- Though amateurish software can still be broken by this kind of testing, it’s rare for professionally created software today.
- It uses the caching techniques of Spark framework to keep the intermediate results in memory while the next iteration is being performed for minimize the number of joins.
- The resultant Pig Latin script is automatically mapped onto a sequence of Hadoop MapReduce jobs by Pig for query execution.
This kind of optimization is efficient for queries which share the same join variable such as star-pattern queries. Rijsbergen (1979) is the earliest book which has dedicated a complete chapter to probabilistic IR. A definitive theoretical resource and a practical guide to text indexing and compression is Witten et al. (1999). Grossman and Frieder (2004) is still a relevant IR reference. It provides an exposition of IR models, tools, cross-language IR, parallel IR, and integrating text with structured data. Belew (2001) offers a cognitive science perspective to the study of information as a computer science discipline using the notion Finding Out About.
Software Testing Podcasts
This book describes the search evolution, and provides an overview of search engines, clustering, classification, content analytics, and visualization. It also discusses IBM Watson’s DeepQA technology and how it was used to answer Jeopardy game questions. Croft et al. (2010) is a very readable introduction to IR and web search engines. Though the focus of this book is on web search engines, it provides an excellent introduction to IR concepts and models. MeTA is an open-source software that accompanies this book, which is intended for enabling readers to quickly run controlled experiments. Another reason I really like the Sun Microsystems Security certification is because there is a lot of crossover between Solaris and Linux systems.
Defines a type-parameterized test suite based on the test fixture
TestFixtureName. Defines a typed test suite based on the test fixture TestFixtureName. The query engine by Sejdiu et al.  uses Jena ARQ for walking through the SPARQL query. The bindings corresponding syntax based testing to a query are used to generate its Spark scala code. The SPARQL query rewriter in this approach uses multiple Spark operations. It firstly, maps the partitioned data to a list of variable bindings that satisfy the first triple pattern of the query.
Model-based testing strategies and their (in)dependence on syntactic model representations
Thus, it performs pruning on the basis of term types and prefixes. Finally, it transforms this algebraic expression into a SQL algebraic expression. The bindings generated during the view or mapping construction phases are used for generating a SQL query from the SPARQL query.