## Filip Niksic awarded ACM SIGPLAN John C. Reynolds Dissertation Award

Filip Niksic's thesis on "Combinatorial Constructions for Effective Testing" has won the John C. Reynolds Doctoral Dissertation Award for 2020. This is an annual award given by ACM SIGPLAN for a doctoral dissertation in the field of programming languages. Filip was advised by MPI-SWS faculty member Rupak Majumdar.

The award citation reads as follows: Soundness is at the core of most programming language verification techniques. On the other hand, random testing is one of the most commonly used techniques for analyzing software. …

The award citation reads as follows: Soundness is at the core of most programming language verification techniques. On the other hand, random testing is one of the most commonly used techniques for analyzing software. …

Filip Niksic's thesis on "Combinatorial Constructions for Effective Testing" has won the John C. Reynolds Doctoral Dissertation Award for 2020. This is an annual award given by ACM SIGPLAN for a doctoral dissertation in the field of programming languages. Filip was advised by MPI-SWS faculty member Rupak Majumdar.

The award citation reads as follows: Soundness is at the core of most programming language verification techniques. On the other hand, random testing is one of the most commonly used techniques for analyzing software. Developing a theory of soundness for random testing is therefore a very important goal, but very few results existed before this thesis.Randomized techniques are seldom used in (sound) program analyses, which means that addressing the problem required the development of new ways to approaching it. Filip Niksic's thesis is among the first to apply deep techniques from randomized algorithms and combinatorics to the problem of understanding and explaining the effectiveness of random testing. Moreover, the theory helps with the design of new random testing approaches. The thesis addresses a hard problem, brining in novel theory from outside programming languages, and proving hard theorems. As scientists, when we see a phenomenon that we cannot immediately explain (in this case, the effectiveness of random testing), we should try to build a scientific explanation. For some problems, including random testing, it is unclear that one can actually formulate a precise theory, because the "real world" is extremely messy. The fact that Filip Niksic is able to formulate such problems precisely and prove nontrivial theorems about them is surprising and opens the door to a new field.

The award citation reads as follows: Soundness is at the core of most programming language verification techniques. On the other hand, random testing is one of the most commonly used techniques for analyzing software. Developing a theory of soundness for random testing is therefore a very important goal, but very few results existed before this thesis.Randomized techniques are seldom used in (sound) program analyses, which means that addressing the problem required the development of new ways to approaching it. Filip Niksic's thesis is among the first to apply deep techniques from randomized algorithms and combinatorics to the problem of understanding and explaining the effectiveness of random testing. Moreover, the theory helps with the design of new random testing approaches. The thesis addresses a hard problem, brining in novel theory from outside programming languages, and proving hard theorems. As scientists, when we see a phenomenon that we cannot immediately explain (in this case, the effectiveness of random testing), we should try to build a scientific explanation. For some problems, including random testing, it is unclear that one can actually formulate a precise theory, because the "real world" is extremely messy. The fact that Filip Niksic is able to formulate such problems precisely and prove nontrivial theorems about them is surprising and opens the door to a new field.