Abstract: Described are techniques for code review using Quantitative Linguistics (QL). The techniques include comparing received code to one or more repositories of code and identifying one or more portions of rare code in the received code that satisfy a rarity threshold relative to the one or more repositories of code. The techniques further include generating annotated code by annotating the received code at the one or more portions of rare code and transmitting the annotated code to a testing platform.

Abstract: Techniques for vector-based identification of software dependency relationships are described herein. An aspect includes determining a first dependency relationship value between a first code segment and a second code segment. Another aspect includes calculating a magnitude vector based on the first dependency relationship value and a second dependency relationship value corresponding to the first code segment and the second code segment. Another aspect includes determining a relationship score for the first code segment and the second code segment based on the magnitude vector and the first dependency relationship value.

Abstract: Techniques for runtime metrics based test ordering in a computer system are described herein. An aspect includes determining a first runtime metric associated with a first module based on the execution of a first test case. Another aspect includes determining a second runtime metric associated with a second module based on the execution of a second test case. Another aspect includes comparing the first runtime metric and the second runtime metric. Another aspect includes determining an order of a plurality of test cases based on the comparison of the first runtime metric and the second runtime metric. Another aspect includes executing the plurality of test cases in the determined order.

Abstract: A method for testing a system under test (SUT) in an active environment to identify cause of a soft failure includes recording a first difference vector by executing a set of test cases on a baseline system and monitoring performance parameters of the baseline system before and after executing the test cases. Each performance record represents differences in the performance parameters of the baseline system from before and after the execution of a corresponding test case. The method further includes, similarly, recording a second difference vector by executing the test cases on the SUT and monitoring performance parameters of the SUT before and after executing the test cases. The method further includes identifying an outlier performance record from the second difference vector by comparing the difference vectors and further, determining a root cause of the soft failure by analyzing a test case corresponding to the outlier.

Abstract: A method for testing a system under test (SUT) in an active environment to identify cause of a soft failure includes recording a first difference vector by executing a set of test cases on a baseline system and monitoring performance parameters of the baseline system before and after executing the test cases. Each performance record represents differences in the performance parameters of the baseline system from before and after the execution of a corresponding test case. The method further includes, similarly, recording a second difference vector by executing the test cases on the SUT and monitoring performance parameters of the SUT before and after executing the test cases. The method further includes identifying an outlier performance record from the second difference vector by comparing the difference vectors and further, determining a root cause of the soft failure by analyzing a test case corresponding to the outlier.

Abstract: A computer-implemented method for generating penetration tests automatically includes parsing an existing system test case, and identifying a particular program call in the system test case. The particular program call can require a particular data access authorization. The method further includes, in response to the system test case including the particular program call, generating a penetration test using a predetermined attack vector. The method further includes executing the penetration test and detecting an unauthorized access being performed during the penetration test. Further, the method includes responsively, sending a notification that identifies the particular program call.

Abstract: Inputs to a system under test (SUT) are modeled as a collection of attribute-value pairs. A set of testcases is executed using an initial set of test vectors that provides complete n-wise coverage of the attribute-value pairs. For each execution of the testcases, for each attribute-value pair, a non-binary success rate (SAV) is computed based on the binary execution results. The method further includes outputting, to a user, in response to the success rate SAV of the attribute-value pair being below a predetermined threshold, an identification of one or more testcases that use the attribute-value pair, wherein the one or more testcases are to be used for diagnosing a soft failure associated with the SUT.

Abstract: Aspects of the invention include mutating each neural network of a portion of a first array of neural networks, wherein each neural network of the first array of neural networks is configured to select a respective sequence of test cases for testing a computing infrastructure. Causing each neural network of a second array of neural networks to select a respective sequence of test cases for testing the computing infrastructure. Generating a child neural network by performing a crossover operation between a mutated neural network of the portion of the first array and a neural network of the second array of neural networks, the child neural network generating a new sequence of test cases for testing the computing infrastructure.

Abstract: An approach to identifying the use of deprecated source code in software repositories and recommending a replacement. Parsing one or more software repositories for software source code, identified as deprecated, by a machine learning model. Responsive to identifying the deprecated software source code, alerting a first one or more software developers responsible for maintaining a software source code module using the deprecated software source code; and recommending an alternative software source code for use in the software source code module to replace the deprecated software source code to the first one or more software developers.

Abstract: Aspects of the invention include determining, by a processor, a code segment of a computer code, analyzing the code segment to determine one or more other code segments associated with the code segment, determining a set of subject matter experts (SMEs) associated with the code segment and the one or more other code segments, obtaining SME data for each SME in the set of SMEs, wherein the SME data comprises a set of attributes associated with the SME, generating, by the processor, a graphical representation of the set of attributes for each SME in the set of SMEs, transforming the graphical representations into an eigenvectors and eigenvalues, and ranking the SMEs based on their associated eigenvectors and eigenvalues.

Abstract: Inputs to a system under test (SUT) are modeled as a collection of attribute-value pairs. A set of testcases is executed using an initial set of test vectors that provides complete n-wise coverage of the attribute-value pairs. For each execution of the testcases, for each attribute-value pair, a non-binary success rate (SAV) is computed based on the binary execution results. The method further includes outputting, to a user, in response to the success rate SAV of the attribute-value pair being below a predetermined threshold, an identification of one or more testcases that use the attribute-value pair, wherein the one or more testcases are to be used for diagnosing a soft failure associated with the SUT.

Abstract: A computer-implemented method includes receiving a portion of code, inputting the received portion of code to a probabilistic data structure to determine whether the portion of code is similar to one or more other portions of code, and based on determining that the one or more other portions are similar to the received portion of code, identifying a number of similar portions of code. The method also includes storing relationship data that associates the similar portions of code with the received portion of code, determining one or more subject matter experts (SMEs) associated with the similar portions of code and the received portion of code and determining correlations between SME data and the relationship data, and storing the SME data and the correlations, the SME data including an indication of the determined SMEs, the relationship data correlating the one or more SMEs to the received portion of code.

Abstract: Aspects of the invention include creating a neural network including neurons to which actions are assigned in representation of test cases. Tests of various instantiations of the neural network are executed for each test case and a state of the neural network after each test is evaluated to determine a fitness score of a corresponding instantiation. Instantiations having fitness scores that exceed a predefined level and identified and the instantiations having the fitness scores that exceed the predefined level are selected for adjustments. The executing, the evaluating, the identifying and the selecting are iteratively repeated in order to obtain desired fitness scores.

Abstract: Techniques for vector-based identification of software dependency relationships are described herein. An aspect includes determining a first dependency relationship value between a first code segment and a second code segment. Another aspect includes calculating a magnitude vector based on the first dependency relationship value and a second dependency relationship value corresponding to the first code segment and the second code segment.

Abstract: Inputs to a system under test (SUT) are modeled as a collection of attribute-value pairs. A set of testcases is executed using an initial set of test vectors that provides complete n-wise coverage of the attribute-value pairs. For each execution of the testcases, for each attribute-value pair, a non-binary success rate (SAV) is computed based on the binary execution results. in response to a success rate of an attribute-value pair being below a predetermined threshold, a subset of testcases that use the attribute-value pair is identified. Further, sets of code paths for the subset of testcases are identified, each set of code path respectively corresponding to a testcase from the subset of testcases. Further, an intersection of the sets of code paths is determined. Code paths of the SUT that are in the intersection, are highlighted to represent a soft failure with the SUT.

Abstract: Techniques for subject matter expert identification for computer software are described herein. An aspect includes receiving a search string. Another aspect includes searching a plurality of library usage data files to determine a selected library corresponding to the search string. Another aspect includes determining a location of a library call corresponding to the selected library in source code. Another aspect includes determining a computer programmer associated with the determined location based on version control metadata corresponding to the source code. Another aspect includes identifying the determined computer programmer as a subject matter expert for the selected library.

Abstract: Techniques for predictive water condition monitoring are described herein. An aspect includes a method that includes monitoring, by one or more processors, at least one water sensor to establish a baseline of a water condition model and monitoring one or more water conditions. A predicted water condition is determined based on the water condition model and the one or more water conditions. An alert is transmitted to one or more devices based on determining that the predicted water condition indicates a predicted contaminant level above a threshold.

Abstract: Embodiments are described for detecting ransomware attacks. Aspects include receiving a memory access request for a memory location. Aspects also include allowing the memory access request based on a determination that the memory location is not present in a decoy address table. Aspects further include terminating a process that made the memory access request based on a determination that the memory location is present in the decoy address table.

Abstract: Described are techniques for code review using Quantitative Linguistics (QL). The techniques include comparing received code to one or more repositories of code and identifying one or more portions of rare code in the received code that satisfy a rarity threshold relative to the one or more repositories of code. The techniques further include generating annotated code by annotating the received code at the one or more portions of rare code and transmitting the annotated code to a testing platform.

Abstract: A system is provided and includes a securable resource, a locking element configured to assume a locked condition in which the securable resource is locked and an unlocked condition in which the securable resource is unlocked, a first controller, which is receptive of an instruction to authorize users to unlock the securable resource, and a common interface to which the first controller and additional controllers, which are independent from the first controller and one another, are tied. The first controller is configured to authenticate the users and to perform operating system (OS) level control of the locking element in accordance with the instruction to authorize users and an authentication of the users by the common interface.