Locating Software Faults Based on Control Flow and Data Dependence

Transcription

1 JOURNAL OF COMPUTERS, VOL. 9, NO. 12, DECEMBER Locatng Software Faults Based on Control Flow and Data Dependence Chenghu Hu Lab of Intellgent Computng and Software Engneerng Zhejang Sc-Tech Unversty Hangzhou Zhejang, , Chna Emal: Zuohua Dng Lab of Intellgent Computng and Software Engneerng Zhejang Sc-Tech Unversty Hangzhou Zhejang, , Chna Emal: Abstract Debuggng software s a dffcult and tmeconsumng work. Fault localzaton technques are becomng extremely mportant. Coverage Based Fault Localzaton (CBFL) s very commonly used n fault locaton technque. Tarantula s a typcal one. It uses the coverage statstcs of faled executon paths and passed executon paths to calculate the suspcousness n the software. However, snce ths technque gnores the data dependency, t s hard to fnd the bugs whch are not n the suspcous code area but have data dependence wth t. In order to mprove the effcency of fault locatng, we combne control flow coverage nformaton and data dependence from program slcng. We valdate our approach expermentally usng Semens benchmark programs. The expermental results show that our approach s more effectve than Tarantula. Index Terms Fault locaton, Control flow, Program slcng,data dependence I. INTRODUCTION As the scope and complexty of software become ncreasngly tremendous, t s an ncreasngly challengng task to locate software faults. Debuggng s the man method to locate fault n software, however, t s tmeconsumng [1]. At present program slcng and program spectrum-based methods are currently the man fault locaton technques. Mark Weser recommended program slcng of error varable to exclude uncorrelated statements [2]. Program slcng s a well-known analyss technque that extracts all statements that affect varables of nterest n the program [3][4][5][6]. An executon slce-based technque as reported n [7] can be effectve n locatng the faults. Coverage based fault locaton (CBFL) technques [8][9][10][11][12] (such as Tarantula, SBI) have been used to support software debuggng, these methods use well desgned test cases to get the program coverage nformaton, then the program spectra of passed and faled executons [13] s contrasted to calculate the suspcousness [9] of ndvdual program Correspondng author: Zuohua Dng. enttes. Fnally, software developers check the program enttes accordng to the statements rankng n terms of ther fault suspcousness. However, there are stll some cases such as avalable approaches make unsatsfactory results, because the coverage statstcs of program enttes are calculated separately. Several studes [14][15] have put forward that such calculaton gnores the propagaton of nfected program states among them, whch may cause that located entty s not the really fault. Le Zhao proposed a context-aware fault localzaton approach FP va control flow analyss [14]. Erc Wong proposed a novel approach usng executon slces and nter-block data dependence to effectvely locate the faults to overcome these problems [16]. In order to further mprove the effcency of fault locaton and decrease the rate of code nspecton, we combne control flow edge coverage wth data dependence based slce technque, and calculate the control flow edge suspcousness, then rank the basc blocks n descendng order of ther suspcousness. Although the bug may not be n the suspcous code area, t s lkely to have relatonshps wth those codes that have data dependence wth the suspcous code area. We contnue to use the data dependence to locate the fault. Fnally, we conduct experments to valdate the effectveness of our approach usng benchmarks. The experment results show that our method s more effectve n locatng a program bug by examnng less code before fndng the really faulty statement than the CBFL technque Tarantula n general. The paper s organzed as follows: In Secton II we ntroduce a smple example wth Tarantula technque. Secton III dscusses our method. In Secton IV, we evaluate our method wth experments. Secton V concludes the paper and dscusses the future work. II. AN EXAMPLE In ths secton, we take a smple example to calculate the code nspecton percentage usng the CBFL technque Tarantula. The program s used to calculate the addton and subtracton of two values; t has a fault n the 7th lne, do: /jcp

2 2798 JOURNAL OF COMPUTERS, VOL. 9, NO. 12, DECEMBER 2014 as we can see from the Fg. 1. Accordng to past studes [2][10][17][18][19], frst, we use some desgned test cases to run the program and get the nformaton whether the program wll pass the test or not. We also get the coverage nformaton of the program code lnes, f a statement s covered by a test case, we wll record the statement as 1, conversely record t as 0. If a statement s covered by more faled test cases, the statement s more lkely to contan the fault. Next, we calculate the suspcousness of each statement, the formula s as below: faled() s totalfaled suspcousness() s passed ( s) faled( s) totalpassed totalfaled The passed(s) represents the number of succeed test cases cover the statement s; faled(s) represents the number of faled test cases cover the statements; totalfaled represents the number of faled test cases; totalpassed represents the number of succeed test cases. Then we rank them n descendng order and examne the statement untl the fault s found. Fnally we get the percentage of code nspecton; the result s about 13.3%. Next, we recommend our method though ths example n the next secton. 1 # nclude <stdo.h> 2 double add (nt x, nt y) { 3 double s=0; 4 f(x>0 && y>0) { 5 s=x+y; 6 } 7 return 2*s;// 正确为 return s; 8 } 9 double sub (nt x, nt y) { 10 double s=0; 11 f(x-y>0) { 12 s=x-y; 13 } 14 return s; 15 } 16 double calculate (nt x, nt y, char op) 17 { 18 double s=0; 19 swtch (op) { 20 case '+': s=add(x, y); break; 21 case '-': s=sub(x, y); break; 22 } 23 return s; 24 } 25 nt man (nt argc, char *args []) { 26 FILE *fp; 27 f (args [1] ==NULL) { 28 fp=stdn; 29 } 30 else { 31 fp=fopen (args [1],"r"); 32 } 33 f (fp==null) { 34 fprntf (stout, "error\n"); 35 ext (0); 36 } 37 nt x, y; 38 char op [10]; 39 fscanf (fp,"%d", &x); 40 fscanf (fp,"%d", &y); 41 fscanf (fp,"%s", op); 42 fclose (fp); 43 double s=calculate(x, y, op [0]); 44 fprntf (stdout,"%f\n", s); 45 return 0; 46 } Fgure 1: The source code of program III. METHODLOGY In ths secton, we ntroduce the basc defntons and related technques frstly, and then explan our method. Besdes, we also take the above example descrbed n Fg. 1 to llustrate the procedure of our method and compare the effcency of fault locaton. A. The Fault Locaton Based on Control Flow Edge We frst ntroduce some defntons, A Control Flow Graph (CFG) s an executon graph G = (B, E), B = {b 1, b 2 b m } represents the basc blocks, whch are consecutve statements or expressons contanng no transfer of control except at the end. E = {e 1, e 2 e k } represents the control flow edges that go from one block to the other. Besdes, we defne Path = {p 1, p 2 p n }, e (b, b j ) represents the edge that goes from block b to b j, e(*, b j ) represents all the edges that go to b j and e (b,*) represents all the edges that start from b. B. The Fault Locaton Based on Program Slcng Technque Program slcng technque s used for decomposton of a program [20]. It s one of fault locaton methods orgnally proposed by Mark Weser [21]. A slce contans a set of statements that mght nfluence the value of a varable v at pont s, whch s based on dependence analyss. Data dependence means that a varable s depended on another statement, or vce versa. C. Caculate the Suspcousness of the Basc Block 1) The theory and method We recommend the fault locaton technque based on control flow edge [14] accordng to a smple example.

3 JOURNAL OF COMPUTERS, VOL. 9, NO. 12, DECEMBER Fgure 2: The program and control flow graph Ths basc block s B ={b 1,b 2,b 3,b 4, b 5 }, the control flow edge s E={e(b 1,b 2 ), e(b 2,b 3 ), e(b 2,b 4 ), e(b 3,b 5 ), e(b 4,b 5 )}, the executon path s Path={b 1 b 2 b 3 b 4, b 1 b 2 b 4 b 5 }. The formula of edge suspcousness calculaton s faled( e ) ( e ) (1) passed ( e ) faled( e ) faled (e ) and passed (e ) respectvely represent the number of faled executons and passed executons that cover e. In ths example, prob n (e(s,d)) represents the proporton of θ(e(s,d)) to θ(e(*,d)), that s the start suspcousness of e(s,d). prob out (e(s,d)) represents the proporton of θ(e(s,d)) to θ(e(s,*)), t represents the destnaton suspcousness of e(s,d) prob e(*, d) n( e( s, d)) (2) e(*, d) ( e( s, d)) [ ( e(*, d))] prob es (,*) out ( e( s, d)) (3) es (,*) ( e( s, d)) [ ( es (,*))] In ths example, we suppose that all the executons that cover e (b 3, b 5 ) succeed and those cover e (b 4, b 5 ) faled. Accordng to (1), we can calculate the proneness of b 4 and b 5, whch means the probablty of a block contanng faults. proness b prob e b b 2 ( e( b, b )) 4 5 ( 4) n( ( 4, 5)) 2 ( e( b4, b5 )) ( e( b3, b5 )) proness b prob e b b ( e( b, b )) 4 5 ( 5) out ( ( 4, 5)) 1 ( e( b4, b5)) Accordng to the fal executon path = {b 1,b 2 b n }, b -1 represents the predecessor block of b, b +1 represents the successor block of b. proness( b ) : proness( b ) 1 probn ( e( b 1, b )) prob ( e( b, b )) out 1 Next, n ths way we can get the rato of fault proneness of blocks from b to b n. proness( b ) : proness( b ) :...: proness( b ) 1 2 prob ( b, b ) prob ( b, b ) = 1: :...: prob ( b, b ) prob ( b, b ) n1 out out 1 n1 n n 1 n Ths rato of proneness s correspondng to a faled executon, consderng the entre program, we add all the fault proneness values whch cover the same block, and then the sum of fault proneness represents the block suspcousness, whch s shown n below formula. suspcousness( b) ( proness ( b) faled( path ) 0 & & b path ) In fact, the structure of a program s often very complex when the program contans crculatons, so the constructon of CFG wll be more complex. Le Zhao s paper ddn t menton ths case especally. In our paper, we make the followng treatment. 1) If the basc block occurs frstly n the executon, we calculate ts suspcousness. 2) If the basc block doesn t occur frstly n the executon, we don t make any changes for the suspcousness. 2) The method mplementaton Frst, we need to record the code coverage nformaton; we use the gcov tool whch GNU gcc compler comes wth to acqure the coverage nformaton. Meanwhle we can get the.gcno fle whch records the basc block and arc nformaton, though whch we can construct the control flow graph. Next, we can get the executon path of a test case by the code lnes coverage nformaton combnng wth the code lnes the basc block correspondng to. Fnally we can calculate the suspcousness of the basc block. D. Construct the Program Slce Based on Data Dependence We get the rankng of the suspcousness of basc blocks accordng to the control flow edge coverage nformaton, make use of varable data dependence relaton and then choose the block whch suspcousness s the bggest as the slce defned as D (1). E f and E p respectvely represent the code set that faled test cases and the succeed test cases cover, E all means all code sets. Next, we examne whether D (1) contans fault or not. If t does, we can locate the fault drectly, f t doesn t contan the fault, we expand the code. W.Erc Wong presents an augmentaton method to nclude addtonal code [16], n hs method, the slce D (1) = E f - E p, θ = E f - D (1), whch means the code area to examne addtonally. In our paper, D (1) represents the basc block whch suspcousness s the bggest. Besdes, we defned θ = E all - D (1), consderng more data dependency. We also defne the data dependence relaton f and only f α defnes a varable used n D (1) or α uses a varable j defned n D (1), that s, a and D (1) have the data dependence relaton, whch s defned as a@ D (1). The process s ntroduced as follows: Step1: Defne the slce S (1), whch s the frst code segment examned, S (1) = {a aθ &( a@d (1) )}. Step2: Set = 1. Step3: Examne the S () to see whether t contans the fault. Step4: If true, that s, the fault s located n the slce, then stop. Step5: Set = +1. Step6: Defne the slce S () = S (-1) {a aθ &( a@s (- 1) )},

4 2800 JOURNAL OF COMPUTERS, VOL. 9, NO. 12, DECEMBER 2014 S () s the code segment of the th teraton. Step7: If S () = S ( -1), that s, no new code segment can be ncluded to the slce, then stop. Step8: Go back to Step3. In ths process, we utlze the data dependence to construct the S (), we can see that S (1) S (2) S (3)... By usng the method descrbed n the above part, we calculate the suspcousness of basc block n the Fg. 1. The result s shown n TABLE I. TABLE I THE SUSPICIOUSNESS OF BASIC BLOCK Functon Basc block Code lne Suspcousness man block1 25,27 0 man block man block man block man block man block man block man block man block man block man block man block man block man block14 vrtual block 2.5 calculate block15 16,18,19 0 calculate block calculate block calculate block calculate block19 vrtual block 0.2 sub block20 9,10,11 0 sub block sub block sub block23 vrtual block 0 add block24 2,3,4 80 add block add block add block27 vrtual block 1.33 Next, we select the bggest suspcousness of basc block as the slce D (1) and examne the statements n t, as we can see n the Table II. Although the fault may not be n the suspcous code area, t s lkely to have relatonshps wth those codes that have data dependence wth t. So f the bug s not n D (1), then we need to examne the addtonal code n S (1), S (2), etc. Fnally, we calculate the percentage of code nspecton. TABLE II THE SLICE D (1) Functon Basc block Code lne Code add block25 5 to 5 s=x+y; We don t fnd the bug by examnng the slce D (1), so we expand the code and get the slce S (1) after 1th teraton, as we can see from the Table III. After examnng the slce S (1), we don t fnd the fault stll, so we contnue to expand the code. The result s shown n Table IV. We fnd the fault n the functon add by examnng the slce S (2), we locate the fault successfully after examne 5 lnes code. We can calculate the percentage of code nspecton, whch s about 10.87%. By contrast, we use TABLE III THE SLICE S (1) Functon Basc block Code lne Code add block25 5 to 5 s=x+y; add block24 2 to 4 nt x,nt y double s=0; TABLE IV THE SLICE S (2) Functon Basc block Code lne Code add block25 5 to 5 s=x+y; add block24 2 to 4 nt x,nt y double s=0; Tarantula whch s the tradtonal fault locaton method based on coverage nformaton to get the locaton effect. From the secton II, we can know the percentage of code nspecton s about 13.3% wth Tarantula. By ths smple example, we can see that the effect of our method s better. In order to further valdate our method, we conclude a seral of experments n next secton. TABLE V STATICS OF SUBJECT PROGRAMS Program Faulty Versons Loc Test Cases prnt_tokens prnt_tokens replace schedule schedule tcas tot_nfo IV. THE EXPERIMENTAL EVALUATION In ths secton we conduct the experments to evaluate the effect of our method. A. Expermental Setup In ths study, we use the Semens sute [22] as the subject programs, whch s the most common test sute used to evaluate the effect of fault localzaton technques. An overall descrpton of Semens sute s presented n Table V. Take the prnt_tokens for example, t has seven faulty versons, and each of them has exactly one fault. The lne of codes are 472, the number of test cases are There are 132 faulty versons of the seven subject programs n total. We select Tarantula [17] whch s a typcal CBFL technque to compare wth our method. In our experments, we all select 115 faulty versons to conduct the experments n accordance wth the paper. We only consder the sngle error n ths experment, so we exclude the verson1 of pnt_tokens, versons2 and 7, verson21 of replace, verson3, 10, 11, 15, 31, 32, 33 and 40 of tcas. The errors n verson4 and 6 of prnt_tokens occur only n the header fles, we also exclude them. Verson 10 of prnt_tokens2, verson9 of schedule2 and verson 32 of replace have no test cases fal, so they are not consdered by our experments. We could collect the coverage nformaton. The experment envronment we use s Ubuntu and the

5 JOURNAL OF COMPUTERS, VOL. 9, NO. 12, DECEMBER compler s gcc4.6.1, we use gcov to collect the coverage nformaton. B. Evaluaton Metrc At present, the evaluaton metrc of fault locaton adopted frequently s the score method represented by Jones [17]. In ths paper we also use the score as the evaluaton metrc, whch means the percentage of statements have to be examned untl the fault s found. Frst, we calculate the suspcousness of the basc block and rank them n a descendng order. Code wth a hgher suspcousness should be examned before that wth a lower suspcousness. The score s defned that (s / S) * 100%, s represents the number of statements we should examne untl fndng the fault and S represents the number of executable statements. In our experments, consderng that the codes we need to examne contan executed and partal non-executed statements, S represents the sze of program. C. The Result Analyss We denote our method based on control flow and data dependence by CFDD. In ths experment, we select the CBFL technque such as Tarantula to compare wth our method CFDD. As we can see from the Fg. 3, the x-axs represents the seral numbers of faults, and we select a part of fault numbers whch s 115 n total, whch reflects the overall condton. The partal fault numbers s shown n the followng fgure. The y-axs ndcates the percentage of code that need to be examned to locate a fault,.e the percentage of code nspecton. If the length of the bar s longer, the correspondng fault locaton technque s less effectve. For example, when the seral numbers of faults s 2, the percentage of code nspecton s about 21% wth the Tarantula method whle our method s about 12.4%, whch represents that CFDD s more effectve clearly. By the Fg. 3, we can also fnd that the CFDD does not always perform well for every fault; sometmes the code nspecton s a lttle hgher than Tarantula technque. In order to llustrate the ssue, we calculate the average code nspecton percentage on the seven programs of the Semens sute. It s shown n the Table VI. As we can see from the Table VI, CFDD method performs better than Tarantula on most of the seven programs, whle the effect of CFDD s lower than Tarantula on two versons. By analyzng the experment results, we fnd that when the code nspecton percentage s low wth Tarantula, our method performs slghtly worse than Tarantula. We can also fnd smlar phenomenon mentoned n Le Zhao s paper. If fault locaton s the place where the falures Fgure 3: The comparson of code nspecton percentage among Tarantula and our method TABLE VI THE COMPARISON OF CODE INSPECTION PERCENTAGE BETWEEN TARANTULA AND OUR METHOD ON THE SIEMENS SUITE verson Av era ge Ta ran tul a CF D D prnt _toke ns prnt _toke ns2 rep lac e sched ule sched ule2 occur, and then the Tarantula s sklful n locatng ths type of errors. When the code nspecton percentage wth Tarantula technque s hgh, our method tends to perform better. Because our method s based on the control flow coverage nformaton, t s good at locatng some complex faults that appear n branch condton. Our method s also based on data dependence; t can locate the type of error such as the varable defnton and the mssng code lne. In Fg. 4, we llustrate the cumulatve dstrbuton of effectveness scores per analyss method where the x-axs ndcates the percentage of code that needs to be examned. The y-axs represents the percentage of the fault verson that fault has been located account for all the fault versons. For example, f the value of horzontal axs s 30, the respondng value of y- axs s 71.3, whch means f we examne 30% code, we can fnd the errors of 71.3% fault versons. From the Fg. 4 we can fnd that although our method does not perform well n all types of faults, our method s very promsng n the fault locaton. When the code nspecton percentage s above 15% approxmately, our method can examne more faulty versons than Tarantula technque. We consder the past studes, such as the FP method based on control flow analyss, Le Zhao combnes the FP wth Tarantula, frst uses the Tarantula technque to locate errors, f the error s not found untl the percentage of code nspecton s up to some value, use the FP nstead. The results show that t s more effectve clearly. tca s tot_ nfo

6 2802 JOURNAL OF COMPUTERS, VOL. 9, NO. 12, DECEMBER 2014 Fgure 4: Cumulatve comparson wth Tarantula on the Semens sute V. CONCLUSIONS AND FUTURE WORK In ths paper, we ntroduce the fault locaton technque based on control flow nformaton and data dependency. We use the control flow paths to analyze the program and calculate the block suspcousness, then we use varable data dependence relaton to choose the bggest suspcous block, then ncrease the search doman to locate the really fault. Fnally, we calculate the percentage of code nspecton. Expermental results show that our approach could fnd bugs wth lower code nspecton by programmers. Our method performs better than the Tarantula. In the future, we plan to study what effect dfferent types of faults and dfferent test cases have on our fault locaton method. We are also tryng to ntegratng wth Tarantula further and see f we can get better results. Besdes, the program n Semens sute only contans one error, what f the program contans multple faults; ths problem s worthy to study further. ACKNOWLEDGMENT Ths work s supported by NSFC under Grants No and REFERENCES [1] Jones, James Arthur. Sem-automatc fault localzaton. Dss. Georga Insttute of Technology, [2] Weser, Mark. "Programmers use slces when debuggng." Communcatons of the ACM 25.7 (1982): [3] Agrawal, Hralal, and Joseph R. Horgan. "Dynamc program slcng." ACM SIGPLAN Notces 25.6 (1990): [4] Kusumoto, Shnj, et al. "Expermental evaluaton of program slcng for fault localzaton." Emprcal Software Engneerng 7.1 (2002): [5] Mock, Markus, et al. "Improvng program slcng wth dynamc ponts-to data." Proceedngs of the 10th ACM SIGSOFT symposum on Foundatons of software engneerng. ACM, [6] Mao, Chunyu. "Dynamc Slcng Research of UML Statechart Specfcatons." Journal of Computers 6.4 (2011): [7] Agrawal, Hralal, Rchard A. DeMllo, and Eugene H. Spafford. "Debuggng wth dynamc slcng and backtrackng." Software: Practce and Experence 23.6 (1993): [8] Abreu, Ru, et al. "A practcal evaluaton of spectrumbased fault localzaton." Journal of Systems and Software (2009): [9] Yu, Yanbng, James A. Jones, and Mary Jean Harrold. "An emprcal study of the effects of test-sute reducton on fault localzaton." Proceedngs of the 30th nternatonal conference on Software engneerng. ACM, [10] Jones, James A., and Mary Jean Harrold. "Emprcal evaluaton of the tarantula automatc fault-localzaton technque." Proceedngs of the 20th IEEE/ACM nternatonal Conference on Automated software engneerng. ACM, [11] Chlmb, Trshul M., et al. "HOLMES: Effectve statstcal debuggng va effcent path proflng." Software Engneerng, ICSE IEEE 31st Internatonal Conference on. IEEE, [12] Santelces, Raul, et al. "Lghtweght fault-localzaton usng multple coverage types." Software Engneerng, ICSE IEEE 31st Internatonal Conference on. IEEE, [13] Jeffrey, Denns, Neelam Gupta, and Rajv Gupta. "Fault localzaton usng value replacement." Proceedngs of the 2008 nternatonal symposum on Software testng and analyss. ACM, [14] Zhao, Le, Lna Wang, and Xaodan Yn. "Context-aware fault localzaton va control flow analyss." Journal of Software 6.10 (2011): [15] Zhang, Zhenyu, et al. "Capturng propagaton of nfected program states." Proceedngs of the the 7th jont meetng of the European software engneerng conference and the ACM SIGSOFT symposum on The foundatons of software engneerng. ACM, [16] Wong, W. Erc, and Yu Q. "Effectve program debuggng based on executon slces and nter-block data dependency." Journal of Systems and Software 79.7 (2006): [17] Jones, James A., Mary Jean Harrold, and John Stasko. "Vsualzaton of test nformaton to assst fault localzaton." Proceedngs of the 24th nternatonal conference on Software engneerng. ACM, [18] Lna Chen. Automatc Test Cases Generaton for Statechart Specfcatons from Semantcs to Algorthm. Journal of Computers, 2011,6 (4): [19] Qan, Zhongsheng. "Test Case Generaton and Optmzaton for User Sesson-based Web Applcaton Testng." Journal of Computers 5.11 (2010): [20] Hong, Hyoung Seok, Insup Lee, and Oleg Sokolsky. "Abstract slcng: A new approach to program slcng based on abstract nterpretaton and model checkng." Source Code Analyss and Manpulaton, Ffth IEEE Internatonal Workshop on. IEEE, [21] Weser, Mark. "Program slcng." Proceedngs of the 5th nternatonal conference on Software engneerng. IEEE Press, [22] Hutchns, Monca, et al. "Experments of the effectveness of dataflow-and controlflow-based test adequacy crtera." Proceedngs of the 16th nternatonal conference on Software engneerng. IEEE Computer Socety Press, Chenghu Hu s a graduate student at the Laboratory of Intellgent Computng and Software Engneerng, Zhejang Sc- Tech Unversty, Hangzhou, Chna. Hs research nterests nclude program analyss and program fault locaton.

7 JOURNAL OF COMPUTERS, VOL. 9, NO. 12, DECEMBER Zuohua Dng receved hs Ph.D. degree n mathematcs and M.S. degree n computer scence from the Unversty of South Florda, Tampa, FL, USA, n 1996 and 1998, respectvely. He s currently a Professor and the Drector of the Laboratory of Intellgent Computng and Software Engneerng, Zhejang Sc-Tech Unversty, Hangzhou, Chna, and has been a Research Professor at the Natonal Insttute for Systems Test and Productvty, USA, snce From 1998 to 2001, he was a Senor Software Engneer wth Advanced Fber Communcaton, USA. Hs research nterests nclude system modelng, program analyss, servce computng and Petr nets, subjects on whch he has authored and coauthored more than 70 papers..