Model driven testing based on test history

We consider software systems consisting of a set of components running as a sequential process. We model such software systems as a special class of transition systems. The difference with existing approaches is that we propose a test procedure based on the structure of the model and the prior test history that can be used for exhaustive testing in an efficient way. On top of that we provide a statistical stopping rule, that is independent of the underlying way of walking through the system, which allows us to stop earlier with a certain statistical reliability.</p

Quality-Aware Learning to Prioritize Test Cases

Software applications evolve at a rapid rate because of continuous functionality extensions, changes in requirements, optimization of code, and fixes of faults. Moreover, modern software is often composed of components engineered with different programming languages by different internal or external teams. During this evolution, it is crucial to continuously detect unintentionally injected faults and continuously release new features. Software testing aims at reducing this risk by running a certain suite of test cases regularly or at each change of the source code. However, the large number of test cases makes it infeasible to run all test cases. Automated test case prioritization and selection techniques have been studied in order to reduce the cost and improve the efficiency of testing tasks. However, the current state-of-art techniques remain limited in some aspects. First, the existing test prioritization and selection techniques often assume that faults are equally distributed across the software components, which can lead to spending most of the testing budget on components less likely to fail rather than the ones highly to contain faults. Second, the existing techniques share a scalability problem not only in terms of the size of the selected test suite but also in terms of the round-trip time between code commits and engineer feedback on test cases failures in the context of Continuous Integration (CI) development environments. Finally, it is hard to algorithmically capture the domain knowledge of the human testers which is crucial in testing and release cycles. This thesis is a new take on the old problem of reducing the cost of software testing in these regards by presenting a data-driven lightweight approach for test case prioritization and execution scheduling that is being used (i) during CI cycles for quick and resource-optimal feedback to engineers, and (ii) during release planning by capturing the testers domain knowledge and release requirements. Our approach combines software quality metrics with code churn metrics to build a regressive model that predicts the fault density of each component and a classification model to discriminate faulty from non-faulty components. Both models are used to guide the testing effort to the components likely to contain the largest number of faults. The predictive models have been validated on eight industrial automotive software applications at Daimler, showing a classification accuracy of 89% and an accuracy of 85.7% for the regression model. The thesis develops a test cases prioritization model based on features of the code change, the tests execution history and the component development history. The model reduces the cost of CI by predicting whether a particular code change should trigger the individual test suites and their corresponding test cases. In order to algorithmically capture the domain knowledge and the preferences of the tester, our approach developed a test case execution scheduling model that consumes the testers preferences in the form of a probabilistic graph and solves the optimal test budget allocation problem both online in the context of CI cycles and offline when planning a release. Finally, the thesis presents a theoretical cost model that describes when our prioritization and scheduling approach is worthwhile. The overall approach is validated on two industrial analytical applications in the area of energy management and predictive maintenance, showing that over 95% of the test failures are still reported back to the engineers while only 43% of the total available test cases are being executed

Quality-Aware Learning to Prioritize Test Cases

Software applications evolve at a rapid rate because of continuous functionality extensions, changes in requirements, optimization of code, and fixes of faults. Moreover, modern software is often composed of components engineered with different programming languages by different internal or external teams. During this evolution, it is crucial to continuously detect unintentionally injected faults and continuously release new features. Software testing aims at reducing this risk by running a certain suite of test cases regularly or at each change of the source code. However, the large number of test cases makes it infeasible to run all test cases. Automated test case prioritization and selection techniques have been studied in order to reduce the cost and improve the efficiency of testing tasks. However, the current state-of-art techniques remain limited in some aspects. First, the existing test prioritization and selection techniques often assume that faults are equally distributed across the software components, which can lead to spending most of the testing budget on components less likely to fail rather than the ones highly to contain faults. Second, the existing techniques share a scalability problem not only in terms of the size of the selected test suite but also in terms of the round-trip time between code commits and engineer feedback on test cases failures in the context of Continuous Integration (CI) development environments. Finally, it is hard to algorithmically capture the domain knowledge of the human testers which is crucial in testing and release cycles. This thesis is a new take on the old problem of reducing the cost of software testing in these regards by presenting a data-driven lightweight approach for test case prioritization and execution scheduling that is being used (i) during CI cycles for quick and resource-optimal feedback to engineers, and (ii) during release planning by capturing the testers domain knowledge and release requirements. Our approach combines software quality metrics with code churn metrics to build a regressive model that predicts the fault density of each component and a classification model to discriminate faulty from non-faulty components. Both models are used to guide the testing effort to the components likely to contain the largest number of faults. The predictive models have been validated on eight industrial automotive software applications at Daimler, showing a classification accuracy of 89% and an accuracy of 85.7% for the regression model. The thesis develops a test cases prioritization model based on features of the code change, the tests execution history and the component development history. The model reduces the cost of CI by predicting whether a particular code change should trigger the individual test suites and their corresponding test cases. In order to algorithmically capture the domain knowledge and the preferences of the tester, our approach developed a test case execution scheduling model that consumes the testers preferences in the form of a probabilistic graph and solves the optimal test budget allocation problem both online in the context of CI cycles and offline when planning a release. Finally, the thesis presents a theoretical cost model that describes when our prioritization and scheduling approach is worthwhile. The overall approach is validated on two industrial analytical applications in the area of energy management and predictive maintenance, showing that over 95% of the test failures are still reported back to the engineers while only 43% of the total available test cases are being executed

Application of Machine Learning and Data Analytics Methods to Detect Interference Effects from Offset Wells

The goal of this thesis is to demonstrate that linear-based data-driven models are innovative and robust. They have the potential to forecast well bottom-hole pressure and identify interference effects between wells. Permanent Downhole Gauges (PDGs) provide a continuous real-time record of pressure and temperature in the downhole environment. These real-time downhole measurements of pressure contain information about the reservoir properties and interactions with offset wells. This work presents a methodology to reproduce well bottom-hole pressure behavior quickly and to forecast future behavior using those measurements. It also identifies the influence of offset wells based on flowrate-pressure measurements using linear data analysis methods. In this methodology, we chose linear-based machine learning methods as they are much faster, more robust, and more easily interpreted. Furthermore, we formulate the functional relationship between flowrate and bottom-hole pressure into linear relationships using superposition techniques and physical flow behavior assumptions. Then, without making any further physical assumptions, we regulate process into two stages — training and testing. Training is the regression phase where the flowrates and pressures are correlated using linear machine learning algorithms. Testing is the extrapolation, or forecasting, of the training model to predict well pressure behavior based on a flowrate history. First, to identify offset well interference effects for a selected well, we reproduce the well’s bottom-hole pressure response using only flowrate and time data for that well. Subsequently, we test the influence of offset wells on the selected well’s bottom-hole pressure response by considering the selected well and offset well’s flowrate history one at a time, until we have examined all possible offset wells. By systematically studying the effects of offset wells on the selected well's bottom-hole pressure, we are able to determine the interference of offset wells using only flowrate histories for the considered wells. We validate the methodology by using a synthetic reservoir model whose behavior (connectivity) is known. We reproduce and forecast the pressure behavior of a selected well and determine the influence of offset wells. Then, we compare the identified interference wells with known answers. We note that there is an agreement between the algorithm’s results and synthetic model. Also, we test the methodology on the actual field cases. We observe agreement between identified interference effects from offset wells using linear-based data analytics method and those determined from the interpretation of multi-well tests and dynamic observations

Model Driven Mutation Applied to Adaptative Systems Testing

Dynamically Adaptive Systems modify their behav- ior and structure in response to changes in their surrounding environment and according to an adaptation logic. Critical sys- tems increasingly incorporate dynamic adaptation capabilities; examples include disaster relief and space exploration systems. In this paper, we focus on mutation testing of the adaptation logic. We propose a fault model for adaptation logics that classifies faults into environmental completeness and adaptation correct- ness. Since there are several adaptation logic languages relying on the same underlying concepts, the fault model is expressed independently from specific adaptation languages. Taking benefit from model-driven engineering technology, we express these common concepts in a metamodel and define the operational semantics of mutation operators at this level. Mutation is applied on model elements and model transformations are used to propagate these changes to a given adaptation policy in the chosen formalism. Preliminary results on an adaptive web server highlight the difficulty of killing mutants for adaptive systems, and thus the difficulty of generating efficient tests.Comment: IEEE International Conference on Software Testing, Verification and Validation, Mutation Analysis Workshop (Mutation 2011), Berlin : Allemagne (2011

Risk management for traffic safety control

This paper offers a range of modelling ideas and techniques from mathematical statistics appropriate for analysing traffic accident data for the East region operation of CLP Power Hong Kong Limited and for the Hong Kong population in general. We further make proposals for alternative ways to record and collect data, and discuss ways to identify the major contributing factors behind accidents. We hope that our findings will enable the design of effective accident prevention strategies for CLP

Modeling the object-oriented software process: OPEN and the unified process

A short introduction to software process modeling is presented, particularly object-oriented modeling. Two major industrial process models are discussed: the OPEN model and the Unified Process model. In more detail, the quality assurance in the Unified Process tool (formally called Objectory) is reviewed

Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World

This report documents the program and the outcomes of GI-Dagstuhl Seminar 16394 "Software Performance Engineering in the DevOps World". The seminar addressed the problem of performance-aware DevOps. Both, DevOps and performance engineering have been growing trends over the past one to two years, in no small part due to the rise in importance of identifying performance anomalies in the operations (Ops) of cloud and big data systems and feeding these back to the development (Dev). However, so far, the research community has treated software engineering, performance engineering, and cloud computing mostly as individual research areas. We aimed to identify cross-community collaboration, and to set the path for long-lasting collaborations towards performance-aware DevOps. The main goal of the seminar was to bring together young researchers (PhD students in a later stage of their PhD, as well as PostDocs or Junior Professors) in the areas of (i) software engineering, (ii) performance engineering, and (iii) cloud computing and big data to present their current research projects, to exchange experience and expertise, to discuss research challenges, and to develop ideas for future collaborations