Abstract: HVAC control system's supervisory control is crucial for energy-efficient thermal comfort in buildings. The control logic is usually specified as ‘if-then-that-else’ rules that capture the domain expertise of HVAC operators, but they often have conflict that may lead to sub-optimal HVAC performance. Embodiments of the present disclosure provide a method and system for optimized Heating, ventilation, and air-conditioning (HVAC) control using domain knowledge combined with Deep Reinforcement Learning (DRL). The system disclosed utilizes Deep Reinforcement Learning (DRL) for conflict resolution in a HVAC control in combination with domain knowledge in form of control logic. The domain knowledge is predefined in an Expressive Decision Tables (EDT) engine via a formal requirement specifier consumable by the EDT engine to capture domain knowledge of a building for the HVAC control.

Abstract: This disclosure relates generally to the field of program slicing, and, more particularly, to a method and system for function call and variable access based relaxed backward slicing. The method discloses a slicing criterion which focuses only on functions called from an entry function. The slicing criteria uses control and data flow information to slice the given entry function with respect to functions called from the body of the entry function and eventually remove all functions not called directly or indirectly from the entry function. The variables modified by calls in the entry function are considered through side-effect while identifying control and data dependence chain within body of entry function. The proposed technique identifies partitions of functions based on the variables accessible in and functions called from the entry function. Thus, unrelated sets of functions with respect to the entry function are computed and divided into different partitions.

Abstract: The present invention generally relates to the field of automated functional testing, and, more particularly, to a method and system for validation of calculation code against calculation specification. Currently the validation is done manually which is time consuming and effort intensive. Embodiments of present disclosure provide an automated method of validation by generating a schema from the calculation specification, retrieving data based on the schema, executing the intermediate calculations, and comparing the result with the output from the calculation code. The method requires minimal manual input and is a fast, simple, resilient and low-code/no-code technique that can be extended to support any type of calculation code and calculation specifications with minimal or no change.

Abstract: This disclosure relates generally to method and system for source code verification using machine learning based strategy prediction. The method receives a source code comprising a plurality of function properties to be verified. Further, the source code is sliced using at least one sequence slicer among a plurality of sequence slicers and a feature vector generator extracts a plurality of feature vectors from each slice. Further, a neural network generates a plurality likelihood of success values by applying a plurality of verification techniques over each feature vector among the plurality of feature vectors to predict a verification strategy to be applied over each slice to be verified. Furthermore, a verification result is displayed as one of a verification successful (S), a verification failure (F) and an unknown (U) when each slice is not verified for the verification strategy run out of time or memory.

Abstract: Enterprise software relies on numerous types and instances of mathematical and logical calculations for proper functioning, which are formally specified in spreadsheets, known as Calculation Specification (CS) sheets, that provide illustrations of the inputs and outputs of the calculation, and are expressed in inputs and formulae applied on them to get values for outputs. The values for such inputs are retrieved by executing ad hoc queries on respective application's database. Present disclosure provides systems/methods to synthesize code using CS sheets by formulating a Programming By Example (PBE) problem for each input involved in the calculation. A query synthesis technique is then implemented that uses natural language processing and enumerative program synthesis techniques to infer queries corresponding to the PBE problem. These queries are filtered based on whether their output is consistent with expected output in PBE.

Abstract: The present invention generally relates to the field of automated functional testing, and, more particularly, to a method and system for validation of calculation code against calculation specification. Currently the validation is done manually which is time consuming and effort intensive. Embodiments of present disclosure provide an automated method of validation by generating a schema from the calculation specification, retrieving data based on the schema, executing the intermediate calculations, and comparing the result with the output from the calculation code. The method requires minimal manual input and is a fast, simple, resilient and low-code/no-code technique that can be extended to support any type of calculation code and calculation specifications with minimal or no change.

Abstract: HVAC control system's supervisory control is crucial for energy-efficient thermal comfort in buildings. The control logic is usually specified as ‘if-then-that-else’ rules that capture the domain expertise of HVAC operators, but they often have conflict that may lead to sub-optimal HVAC performance. Embodiments of the present disclosure provide a method and system for optimized Heating, ventilation, and air-conditioning (HVAC) control using domain knowledge combined with Deep Reinforcement Learning (DRL). The system disclosed utilizes Deep Reinforcement Learning (DRL) for conflict resolution in a HVAC control in combination with domain knowledge in form of control logic. The domain knowledge is predefined in an Expressive Decision Tables (EDT) engine via a formal requirement specifier consumable by the EDT engine to capture domain knowledge of a building for the HVAC control.

Abstract: Sub-systems of air handling units in infrastructures face unresolved problem of conflict in the rules that activate in a contradictory manner at the same time resulting in sub-optimal performance of the subsystems. The present disclosure provides a system and method for optimizing performance parameters of air handling units in infrastructures. Rule sets having conflicting conditions are identified after verification of rules which are specific to air handling units. Further, frequency of the rule sets having conflicting conditions is determined to generate a ranked list of the rule sets having conflicting conditions. Another ranking procedure is implemented for the rules comprised in the ranked list of the rule sets having conflicting conditions. The system dynamically optimizes one or more parameters specific to the performance criteria based on the ranking of rules.

Abstract: This disclosure relates generally to the field of program slicing, and, more particularly, to a method and system for function call and variable access based relaxed backward slicing. The method discloses a slicing criterion which focuses only on functions called from an entry function. The slicing criteria uses control and data flow information to slice the given entry function with respect to functions called from the body of the entry function and eventually remove all functions not called directly or indirectly from the entry function. The variables modified by calls in the entry function are considered through side-effect while identifying control and data dependence chain within body of entry function. The proposed technique identifies partitions of functions based on the variables accessible in and functions called from the entry function. Thus, unrelated sets of functions with respect to the entry function are computed and divided into different partitions.

Abstract: Systems and methods for machine learning feature processing are disclosed. An example method includes: selecting a predefined number of machine-based transactions; identifying a first set of features based on the predefined number of machine-based transactions; and applying a machine learning feature selection algorithm to identify a second set of features. The second set of features is smaller than the first set of features, and the second set of features includes no more than a predefined number of features. The method also includes constructing a decision tree based on the second set of features, wherein the decision tree has a depth that is less than a predefined depth. The method optionally includes limiting the total number of features in the second set to no greater than the predefined number of features.

Abstract: A system and a method for verification of a source code are provided. There as many techniques available that can be used for verification of software codes, however, it is difficult to determine appropriate technique that can be utilized for verification of a given software code. In an embodiment, the system receives a source code encoded with one or more specifications to be verified. A static analysis of the source code is performed to identify program features of the source code. The program features may include, but are not limited to, multiple return paths, loops with an unstructured control flow, loops with arrays, short ranges and numerical loops. Based on the identification of the program features, verification techniques are applied to the source code for the verification. Each verification technique of the one or more verification techniques is applied for a predetermined period of time and in a predefined order.

Abstract: Software verification is a vital process to ensure reliability and robustness of software systems. The software verification is associated with verifying one or more properties associated with a piece of code. Conventional methods are unable to verify properties of piece of code with loops, especially in the presence of loops with a large, unknown or infinite bound, or a large number of complex conditions. The system receives an abstracted piece of code corresponding to an original piece of code to check whether the abstracted piece of code hence the original piece of code is safe or not. The system applies model checking over the abstracted piece of code to check one or more property assertions associated with an abstracted piece of code. If the property assertion fails in model checking, a trace leading to the violation of the one or more property assertions is identified and analyzed.

Abstract: Identifying policy violations for controlling behavior of an Internet of Things (IoT) automation system is provided. Traditional systems and methods provide for controlling IoT based automation system based upon a static analysis of a system model and rules. The embodiments of the proposed disclosure provide for controlling behavior of the IoT automation system by identifying one or more policy violations, wherein the one or more policy violations are identified by generating a plurality of models representing behavior, relationships and functions of one or more sub-systems corresponding to the IoT automation system; extracting a set of modelled rules; constructing, using each of the plurality of models and the set of modelled rules, an integrated model; and identifying, from the integrated model, the one or more policy violations via a Model Verifier Component for controlling behavior of the IoT automation system.

Abstract: Software verification is a vital process to ensure reliability and robustness of software systems. The software verification is associated with verifying one or more properties associated with a piece of code. Conventional methods are unable to verify properties of piece of code with loops, especially in the presence of loops with a large, unknown or infinite bound, or a large number of complex conditions. The present disclosure receives an abstracted piece of code corresponding to an original piece of code to check whether the abstracted piece of code hence the original piece of code is safe or not. The system applies model checking over the abstracted piece of code to check one or more property assertions associated with an abstracted piece of code. If the property assertion fails in model checking, a trace leading to the violation of the one or more property assertions is identified and analyzed.

Abstract: A system and a method for verification of a source code are provided. There as many techniques available that can be used for verification of software codes, however, it is difficult to determine appropriate technique that can be utilized for verification of a given software code. In an embodiment, the system receives a source code encoded with one or more specifications to be verified. A static analysis of the source code is performed to identify program features of the source code. The program features may include, but are not limited to, multiple return paths, loops with an unstructured control flow, loops with arrays, short ranges and numerical loops. Based on the identification of the program features, verification techniques are applied to the source code for the verification. Each verification technique of the one or more verification techniques is applied for a predetermined period of time and in a predefined order.

Abstract: The present disclosure generates a plurality of time bound test cases based on an Artificial Rain Drop (ARD) algorithm. Here, events associated with an event-based system are compiled in a tabular format. Each of the plurality of events are represented as a regular expression. Further, timed finite automaton is constructed for each regular expression prior to applying the ARD algorithm.

Abstract: Product testing ensures whether the product is defect free or not and it is an important part of any product before product release. Any inadequacy in testing can result in financial losses and also damage the reputation, brand, and business. Functional testing is important since it verifies that the products functioning meets its requirements. Conventional methods mainly focus on executing test cases rather than generating test cases. Hence there is challenge to create scalable test cases for products with huge volume of data and with complex features. The present disclosure generates a plurality of time bound test cases based on an Artificial Rain Drop (ARD) algorithm. Here, events associated with an event based system are compiled in a tabular format. Each of the plurality of events are represented as a regular expression. Further, timed finite automaton is constructed for each regular expression prior to applying the ARD algorithm.

Abstract: Sub-systems of air handling units in infrastructures face unresolved problem of conflict in the rules that activate in a contradictory manner at the same time resulting in sub-optimal performance of the subsystems. The present disclosure provides a system and method for optimizing performance parameters of air handling units in infrastructures. Rule sets having conflicting conditions are identified after verification of rules which are specific to air handling units. Further, frequency of the rule sets having conflicting conditions is determined to generate a ranked list of the rule sets having conflicting conditions. Another ranking procedure is implemented for the rules comprised in the ranked list of the rule sets having conflicting conditions. The system dynamically optimizes one or more parameters specific to the performance criteria based on the ranking of rules.

Abstract: Identifying policy violations for controlling behavior of an Internet of Things (IoT) automation system is provided. Traditional systems and methods provide for controlling IoT based automation system based upon a static analysis of a system model and rules. The embodiments of the proposed disclosure provide for controlling behavior of the IoT automation system by identifying one or more policy violations, wherein the one or more policy violations are identified by generating a plurality of models representing behavior, relationships and functions of one or more sub-systems corresponding to the IoT automation system; extracting a set of modelled rules; constructing, using each of the plurality of models and the set of modelled rules, an integrated model; and identifying, from the integrated model, the one or more policy violations via a Model Verifier Component for controlling behavior of the IoT automation system.

Abstract: This disclosure relates generally to data structure abstraction, and more particularly to method and system for data structure abstraction for model checking. In one embodiment, the method includes identifying data structure accesses in the source code. Loops are identified in the data structure accesses, and loop-types are identified in the loops. An abstracted code is generated based on the loop types for abstracting the data structure. Abstracting the data structure includes, for each loop, replacing the data structure accesses by one of a corresponding representative element and a non-deterministic value in the loop body of said loop based on elements accessed, and eliminating loop control statement of said loop operating on elements of data structure based on loop type of said loop, and adding a plurality of non-array assignments at a start and after the loop body of the source code. The abstracted code is provided for the model checking.