A framework to distil SQL queries out of host languages in order to apply quality metrics

Transcription

1 Master s Thesis defence A framework to distil SQL queries out of host languages in order to apply quality metrics Department of Information and Computing Sciences Universiteit Utrecht January 11, 2007

2 Master s Thesis defence Outline 1 Introduction 2 Query extraction and reconstruction 3 Detecting relationships among queries, host language and database 4 Software quality metrics 5 Case study 6 Conclusion

3 Master s Thesis defence > Introduction Introduction Software Improvement Group Automatic program analysis 1 Software Risk Assessment 2 Software Monitoring (software change management) 3 Documentation Generation

4 Master s Thesis defence > Introduction Problem description Observations 1 Dynamically constructed SQL queries 2 Unstructured use of the Structured Query Language 3 Untyped use of SQL references 4 Softly mapped on database scheme 5 Decreased maintainability 6 Adapting queries imposes risk

5 Master s Thesis defence > Introduction Problem description Observations 1 Dynamically constructed SQL queries 2 Unstructured use of the Structured Query Language 3 Untyped use of SQL references 4 Softly mapped on database scheme 5 Decreased maintainability 6 Adapting queries imposes risk SQL queries are regarded as easy

6 Master s Thesis defence > Introduction Opportunities for improvement Gaining insight 1 Extract and reconstruct dynamically constructed queries 2 Analysing relations between data, queries and host language 3 Measure quality and complexity

7 Master s Thesis defence > Introduction Opportunities for improvement Gaining insight 1 Extract and reconstruct dynamically constructed queries 2 Analysing relations between data, queries and host language 3 Measure quality and complexity Initial situation 1 No insight in quantities of embedded queries 2 No indication of structural complexity 3 No indication of interaction with host language 4 Means to tokenize and or parse several languages 5 Means to parse several SQL dialects

8 Master s Thesis defence > Query extraction and reconstruction Extract SQL queries out of host languages The objective is to obtain all embedded queries SQL embedded as a language construct 1 COBOL 2 PL/SQL Host languages using strings to construct queries 1 Visual Basic 6 2 Java

9 Master s Thesis defence > Query extraction and reconstruction Extract SQL queries out of host languages Objective For queries composed by the means of string concatenation 1 Collect strings 2 Inspect the strings 3 Reconstruct the queries

10 Master s Thesis defence > Query extraction and reconstruction Collecting queries Detecting query start positions Concerning data SELECT INSERT UPDATE DELETE Concerning structure CREATE ALTER DROP

11 Master s Thesis defence > Query extraction and reconstruction Reconstructing queries by string literal analysis Different languages all use same mechanisms to construct strings Language specifics String concatenation a + b a b a & b String assignment How is the query assigned to a variable String composition + =. = & = String.Concat( a, b )

12 Master s Thesis defence > Query extraction and reconstruction Reconstructing queries by string literal analysis 1 SELECT x FROM y WHERE z 2 strquery = " SELECT "; 3 strquery = strquery & " x " 4 strquery &= " FROM y " 5 strquery = String. Concat ( strquery, " WHERE z "); 6 7 strquery = " SELECT a FROM b WHERE a IN ( " & strquery & " ) "

13 Master s Thesis defence > Query extraction and reconstruction Reconstructing queries by string literal analysis 1 SELECT x FROM y WHERE z 2 strquery = " SELECT "; 3 strquery = strquery & " x " 4 strquery &= " FROM y " 5 strquery = String. Concat ( strquery, " WHERE z "); 6 7 strquery = " SELECT a FROM b WHERE a IN ( " & strquery & " ) " 1 SELECT a FROM b WHERE a IN 2 ( 3 SELECT x FROM y WHERE z 4 )

14 Master s Thesis defence > Query extraction and reconstruction Collecting variables Mechanisms used to expand the query string Primitive types Variables Function calls

15 Master s Thesis defence > Query extraction and reconstruction Collecting variables Mechanisms used to expand the query string Primitive types Variables Function calls Anonymous parameters 1 Frameworks may be involved 2 Function call perform(query, param) 3 Match provided variables with number of question marks 1 strsql = " SELECT a FROM?" 2 Database. Get ( strsql, varname, Null )

16 Master s Thesis defence > Query extraction and reconstruction Collecting variables Variables in PL/SQL 1 No string concatenation present 2 Grammar includes the Structured Query Language 3 Collecting variables used in PL/SQL queries not straightforward 4 Variables indistinguishable from tables and columns

17 Master s Thesis defence > Query extraction and reconstruction Collecting variables Variables in PL/SQL 1 No string concatenation present 2 Grammar includes the Structured Query Language 3 Collecting variables used in PL/SQL queries not straightforward 4 Variables indistinguishable from tables and columns 1 PROCEDURE ins ( df$rec IN OUT df$row_type ) IS 2 BEGIN 3 SELECT MH_NV_ID 4 FROM MH_MT 5 WHERE ID = df$rec. ID ; 6 END ins ; Shadowing mechanism Column reference Variable Table reference

18 Master s Thesis defence > Query extraction and reconstruction Collecting variables Variables in PL/SQL 1 No string concatenation present 2 Grammar includes the Structured Query Language 3 Collecting variables used in PL/SQL queries not straightforward 4 Variables indistinguishable from tables and columns 1 PROCEDURE ins ( df$rec IN OUT df$row_type ) IS 2 BEGIN 3 SELECT MH_NV_ID 4 FROM MH_MT 5 WHERE ID = df$rec. ID ; 6 END ins ; Shadowing mechanism Column reference Variable Table reference Without database scheme no shadowing is assumed

19 Master s Thesis defence > Query extraction and reconstruction Collecting result variables Result variables 1 Applies only when SELECT queries are involved SELECT INTO Cursor ResultSet or RecordSet 1 EXEC SQL 2 SELECT 3 CINFCSTN, 4 CINFFXTR 5 INTO 6 : CINFCSTN, 7 : CINFFXTR 8 FROM IABSTJNF 9 WHERE CINFCSTN = END-EXEC.

20 Master s Thesis defence > Detecting relationships among queries, host language and database Relations among queries, host language and database Now queries are collected, detect relations with respect to queries, host language and database

21 Master s Thesis defence > Detecting relationships among queries, host language and database Operations performed on tables Context sensitive CRUD table 1 Visualises the tables used by a query 2 Visualises the operation type performed 1 INSERT INTO Banned ( uid, username ) 2 SELECT id, name 3 FROM User 4 WHERE id =?; 1 SELECT age 2 FROM User 3 WHERE gender =?; Create Read Update Delete Banned {Q1} User {Q1, Q2}

22 Master s Thesis defence > Detecting relationships among queries, host language and database Relations among data in a database Indicate relations among data in a database Find related columns Detect specified join constructs 1 SELECT Permission. rwx 2 FROM Groups, Permission, User 3 WHERE Permission. gid = Groups. id 4 AND User. uid = Groups. user 5 AND User. name =?

23 Master s Thesis defence > Detecting relationships among queries, host language and database Relations among data in a database Indicate relations among data in a database Find related columns Detect specified join constructs 1 SELECT Permission. rwx 2 FROM Groups, Permission, User 3 WHERE Permission. gid = Groups. id 4 AND User. uid = Groups. user 5 AND User. name =? Approach 1 Table.column = Table.column join 2 Table and column information explicitly supplied 3 Or database scheme present

24 Master s Thesis defence > Detecting relationships among queries, host language and database Relations among data in a database Limitations 1 Regards Grous.user = Groups.user as join 2 Regards A.x = B.y OR A.x = B.y as 2 joins 3 Does not know if the subset actually is used to produce the query result

25 Master s Thesis defence > Detecting relationships among queries, host language and database Relations among data in a database Limitations 1 Regards Grous.user = Groups.user as join 2 Regards A.x = B.y OR A.x = B.y as 2 joins 3 Does not know if the subset actually is used to produce the query result 1 SELECT Permission. rwx 2 FROM Permission, User, Groups 3 WHERE Permission. type = -1 4 OR User. uid = Groups. user 5 AND User. loginname =?; A join is present, but not with regard to the query result

26 Master s Thesis defence > Detecting relationships among queries, host language and database Relations among data in a database Limitations 1 Regards Grous.user = Groups.user as join 2 Regards A.x = B.y OR A.x = B.y as 2 joins 3 Does not know if the subset actually is used to produce the query result 1 SELECT Permission. rwx 2 FROM Permission, User, Groups 3 WHERE Permission. type = -1 4 OR User. uid = Groups. user 5 AND User. loginname =?; A join is present, but not with regard to the query result More accurate and extensive solution provided in my thesis paper

27 Master s Thesis defence > Detecting relationships among queries, host language and database Relations established by control flow Observing query results 1 Query results in IF statements may create dependency 2 Query results in (inner) loop conditions

28 Master s Thesis defence > Detecting relationships among queries, host language and database Relations established by control flow Observing query results 1 Query results in IF statements may create dependency 2 Query results in (inner) loop conditions 1 SELECT MH_MN_SEQ. nextval 2 INTO df$rec. ID 3 FROM DUAL ; 4 5 IF df$rec. ID IS NULL THEN 6 BEGIN 7 SELECT MH_MT_HUIZ. JND_GOE 8 INTO df$jnd. ID 9 FROM DUAL ; IF df$jnd. ID = FALSE THEN 12 df$jnd. ID := TRUE ; 13 END IF; 14 END ; 15 END IF;

29 Master s Thesis defence > Detecting relationships among queries, host language and database Query result may indicate inefficiency Decomposed queries 1 Queries depending on a direct query result may indicate time inefficient approach 2 DBMS optimised for filtering a subset 3 Combined queries however impose increased complexity 1 SELECT age 2 FROM User 3 WHERE name = Alice ; 1 SELECT name 2 FROM User 3 WHERE age =?;

30 Master s Thesis defence > Detecting relationships among queries, host language and database Query result may indicate inefficiency Decomposed queries 1 Queries depending on a direct query result may indicate time inefficient approach 2 DBMS optimised for filtering a subset 3 Combined queries however impose increased complexity 1 SELECT age 2 FROM User 3 WHERE name = Alice ; 1 SELECT name 2 FROM User 3 WHERE age =?; 1 SELECT name 2 FROM User 3 WHERE age in ( SELECT age 4 FROM User 5 WHERE name = Alice );

31 Master s Thesis defence > Detecting relationships among queries, host language and database Quality on handling related data Query results used by other queries 1 Dataflow from SELECT to INSERT 2 Dataflow from SELECT to DELETE Possible manually performed cascade delete Cascade delete normally defined in database scheme Cascade delete invisible in single query

32 Master s Thesis defence > Software quality metrics Measurements that matter Define measurements that indicate quality and complexity

33 Master s Thesis defence > Software quality metrics Measurements that matter Define measurements that indicate quality and complexity Motivation 1 Understandability reduces bugs 2 Complexity decreases testing quality 3 Simplicity benefits maintainability and adaptability

34 Master s Thesis defence > Software quality metrics Measurements that matter Define measurements that indicate quality and complexity Motivation 1 Understandability reduces bugs 2 Complexity decreases testing quality 3 Simplicity benefits maintainability and adaptability Complex parts are opportunities for improvement of the software quality and reliability

35 Master s Thesis defence > Software quality metrics Query occurrences Occurrence per file 1 Quantities 2 Expected in persistence layer, not in domain and GUI 3 Centralized handling of queries SEL INS UPD DEL CRE ALT DROP TCDMUY07.COB PJT PARAM.trg

36 Master s Thesis defence > Software quality metrics Parameters used by queries Number of result variables 1 SELECT INTO 2 Cursor Number of parameters 1 Amount of variables referenced 2 Amount of variable places in a query 1 WHERE User. gender = : gen 2 OR Groups. gender = : gen

37 Master s Thesis defence > Software quality metrics Parameters used by queries Context of the variable 1 WHERE clause 2 Select item 3 HAVING clause 4 GROUP BY clause 5 FROM clause Line WHERE Sel. HAV GROUP FROM mwxf pk.pks dump65.out

38 Master s Thesis defence > Software quality metrics Concerning query structure Equality among queries 1 Collect queries the most similar 2 Collect query parts used in many other queries

39 Master s Thesis defence > Software quality metrics Concerning query structure Equality among queries 1 Collect queries the most similar 2 Collect query parts used in many other queries Q1 Q2 Q3 Q Q Q Most similar queries Q1 with Q2 80 Q2 with Q3 40 Q1 with Q3 10 Similar query parts Q2 120 Q1 90 Q3 50

40 Master s Thesis defence > Software quality metrics Concerning query structure Amount of tables used 1 Count nr of tables used per query 2 Derived from CRUD table Dependant columns referenced in a query Count the nr of JOIN keywords used Collect and count the specified join constructs

41 Master s Thesis defence > Software quality metrics Measuring relations Composition of queries 1 Nr of inner queries 2 Level of nesting 3 Nr of UNION keywords per query Dependant queries Count the nr of depending queries SELECT - DELETE SELECT - INSERT Applies only when SELECT queries are involved

42 Master s Thesis defence > Software quality metrics Measurements Conclusion 1 Per measurement only focus at one aspect 2 Worst query easily identified per measurement 3 Indications to be verified by hand

43 Master s Thesis defence > Software quality metrics Measurements Conclusion 1 Per measurement only focus at one aspect 2 Worst query easily identified per measurement 3 Indications to be verified by hand Metrics reflecting quality 1 Amount of variables - quality SQL usage 2 Amount of tables and unions used - quality data model 3 Nesting level - complexity & testability 4 Equality and dependencies - maintainability

44 Master s Thesis defence > Case study Case study results Used commercial software systems 1 Visual Basic 6 source of a telecom company 2 PL/SQL source of a bank 3 PL/SQL source of an energy supplier 4 COBOL source of a bank

45 Master s Thesis defence > Case study Case study PL/SQL source of an energy supplier Query quantities amount SEL INS UPD DEL All dump dump dump

46 Master s Thesis defence > Case study Case study PL/SQL source of an energy supplier Query quantities amount SEL INS UPD DEL file line Allres vars 5475 vars3427 nest787union 912tbls245 eq deps dump444 dump % 0 dump359 dump % 0 dump249 dump % 0 dump % 0 dump % 0 dump % 37 One variable selected column encountered

47 Master s Thesis defence > Case study Case study PL/SQL source of an energy supplier Query quantities amount SEL INS UPD DEL All dump dump dump All dump dump222 6 dump208 15

48 Master s Thesis defence > Case study Case study COBOL source of a bank Query quantities amount SEL INS UPD DEL All x.cob y.cob z.cob

49 Master s Thesis defence > Case study Case study COBOL source of a bank Query quantities Query quality amount SEL INS UPD DEL All x.cob y.cob z.cob file line res vars vars nesting union tables equality x.cob % x.cob % x.cob % w.cob %

50 Master s Thesis defence > Conclusion Conclusion The results of the measurements do reflect the situation as present in the software Characteristics 1 Undesirable situations are detected 2 Not all indications are truly undesirable 3 Detecting, but not preventing bad situations 4 Not detecting poor strategy of database access

51 Master s Thesis defence > Conclusion Conclusion The results of the measurements do reflect the situation as present in the software Characteristics 1 Undesirable situations are detected 2 Not all indications are truly undesirable 3 Detecting, but not preventing bad situations 4 Not detecting poor strategy of database access Future work 1 Analyses of the supported programming languages immature 2 Improve data flow analysis 3 Expand interprocedural analysis

52 Master s Thesis defence > Conclusion Conclusion Conclusion 1 Facilitates means to focus on certain aspects 2 Highly language independent 3 Framework independent 4 Source is allowed to be incomplete 5 Database scheme may be omitted 6 A flexible effective approach has been achieved 7 Under-investigated research area 8 The topic of SQL metrics does matter

53 Master s Thesis defence > Conclusion Thank you for your attention