39 research outputs found
A Decidable Timeout based Extension of Propositional Linear Temporal Logic
We develop a timeout based extension of propositional linear temporal logic
(which we call TLTL) to specify timing properties of timeout based models of
real time systems. TLTL formulas explicitly refer to a running global clock
together with static timing variables as well as a dynamic variable abstracting
the timeout behavior. We extend LTL with the capability to express timeout
constraints. From the expressiveness view point, TLTL is not comparable with
important known clock based real-time logics including TPTL, XCTL, and MTL,
i.e., TLTL can specify certain properties, which cannot be specified in these
logics (also vice-versa). We define a corresponding timeout tableau for
satisfiability checking of the TLTL formulas. Also a model checking algorithm
over timeout Kripke structure is presented. Further we prove that the validity
checking for such an extended logic remains PSPACE-complete even in the
presence of timeout constraints and infinite state models. Under discrete time
semantics, with bounded timeout increments, the model-checking problem that if
a TLTL-formula holds in a timeout Kripke structure is also PSPACE complete. We
further prove that when TLTL is interpreted over discrete time, it can be
embedded in the monadic second order logic with time, and when TLTL is
interpreted over dense time without the condition of non-zenoness, the
resulting logic becomes -complete
A Randomized Algorithm for 3-SAT
In this work we propose and analyze a simple randomized algorithm to find a
satisfiable assignment for a Boolean formula in conjunctive normal form (CNF)
having at most 3 literals in every clause. Given a k-CNF formula phi on n
variables, and alpha in{0,1}^n that satisfies phi, a clause of phi is critical
if exactly one literal of that clause is satisfied under assignment alpha.
Paturi et. al. (Chicago Journal of Theoretical Computer Science 1999) proposed
a simple randomized algorithm (PPZ) for k-SAT for which success probability
increases with the number of critical clauses (with respect to a fixed
satisfiable solution of the input formula). Here, we first describe another
simple randomized algorithm DEL which performs better if the number of critical
clauses are less (with respect to a fixed satisfiable solution of the input
formula). Subsequently, we combine these two simple algorithms such that the
success probability of the combined algorithm is maximum of the success
probabilities of PPZ and DEL on every input instance. We show that when the
average number of clauses per variable that appear as unique true literal in
one or more critical clauses in phi is between 1 and 1.9317, combined algorithm
performs better than the PPZ algorithm
Data-Driven Application Maintenance: Views from the Trenches
In this paper we present our experience during design, development, and pilot
deployments of a data-driven machine learning based application maintenance
solution. We implemented a proof of concept to address a spectrum of
interrelated problems encountered in application maintenance projects including
duplicate incident ticket identification, assignee recommendation, theme
mining, and mapping of incidents to business processes. In the context of IT
services, these problems are frequently encountered, yet there is a gap in
bringing automation and optimization. Despite long-standing research around
mining and analysis of software repositories, such research outputs are not
adopted well in practice due to the constraints these solutions impose on the
users. We discuss need for designing pragmatic solutions with low barriers to
adoption and addressing right level of complexity of problems with respect to
underlying business constraints and nature of data.Comment: Earlier version of paper appearing in proceedings of the 4th
International Workshop on Software Engineering Research and Industrial
Practice (SER&IP), IEEE Press, pp. 48-54, 201