Finding Faulty Functions From the Traces of Field Failures

Corrective maintenance, which rectifies field faults, consumes 30-60% time of software maintenance. Literature indicates that 50% to 90% of the field failures are rediscoveries of previous faults, and that 20% of the code is responsible for 80% of the faults. Despite this, identification of the location of the field failures in system code remains challenging and consumes substantial (30-40%) time of corrective maintenance. Prior fault discovery techniques for field traces require many pass-fail traces, discover only crashing failures, or identify faulty coarse grain code such as files as the source of faults. This thesis (which is in the integrated article format) first describes a novel technique (F007) that focuses on identifying finer grain faulty code (faulty functions) from only the failing traces of deployed software. F007 works by training the decision trees on the function-call level failed traces of previous faults of a program. When a new failed trace arrives, F007 then predicts a ranked list of faulty functions based on the probability of fault proneness obtained via the decision trees. Second, this thesis describes a novel strategy, F007-plus, that trains F007 on the failed traces of mutants (artificial faults) and previous faults. F007-plus facilitates F007 in discovering new faulty functions that could not be discovered because they were not faulty in the traces of previously known actual faults. F007 (including F007-plus) was evaluated on the Siemens suite, Space program, four UNIX utilities, and a large commercial application of size approximately 20 millions LOC. F007 (including the use of F007-plus) was able to identify faulty functions in approximately 90% of the failed traces by reviewing approximately less than 10% of the code (i.e., by reviewing only the first few functions in the ranked list). These results, in fact, lead to an emerging theory that a faulty function can be identified by using prior traces of at least one fault in that function. Thus, F007 and F007-plus can correctly identify faulty functions in the failed traces of the majority (80%-90%) of the field failures by using the knowledge of faults in a small percentage (20%) of functions

Dynamic Analysis can be Improved with Automatic Test Suite Refactoring

Context: Developers design test suites to automatically verify that software meets its expected behaviors. Many dynamic analysis techniques are performed on the exploitation of execution traces from test cases. However, in practice, there is only one trace that results from the execution of one manually-written test case. Objective: In this paper, we propose a new technique of test suite refactoring, called B-Refactoring. The idea behind B-Refactoring is to split a test case into small test fragments, which cover a simpler part of the control flow to provide better support for dynamic analysis. Method: For a given dynamic analysis technique, our test suite refactoring approach monitors the execution of test cases and identifies small test cases without loss of the test ability. We apply B-Refactoring to assist two existing analysis tasks: automatic repair of if-statements bugs and automatic analysis of exception contracts. Results: Experimental results show that test suite refactoring can effectively simplify the execution traces of the test suite. Three real-world bugs that could previously not be fixed with the original test suite are fixed after applying B-Refactoring; meanwhile, exception contracts are better verified via applying B-Refactoring to original test suites. Conclusions: We conclude that applying B-Refactoring can effectively improve the purity of test cases. Existing dynamic analysis tasks can be enhanced by test suite refactoring

Privacy-Friendly Mobility Analytics using Aggregate Location Data

Location data can be extremely useful to study commuting patterns and disruptions, as well as to predict real-time traffic volumes. At the same time, however, the fine-grained collection of user locations raises serious privacy concerns, as this can reveal sensitive information about the users, such as, life style, political and religious inclinations, or even identities. In this paper, we study the feasibility of crowd-sourced mobility analytics over aggregate location information: users periodically report their location, using a privacy-preserving aggregation protocol, so that the server can only recover aggregates -- i.e., how many, but not which, users are in a region at a given time. We experiment with real-world mobility datasets obtained from the Transport For London authority and the San Francisco Cabs network, and present a novel methodology based on time series modeling that is geared to forecast traffic volumes in regions of interest and to detect mobility anomalies in them. In the presence of anomalies, we also make enhanced traffic volume predictions by feeding our model with additional information from correlated regions. Finally, we present and evaluate a mobile app prototype, called Mobility Data Donors (MDD), in terms of computation, communication, and energy overhead, demonstrating the real-world deployability of our techniques.Comment: Published at ACM SIGSPATIAL 201

Disseminated sulphide mineralisation at Garbh Achadh, Argyllshire, Scotland

A brief investigation of low-grade copper mineralisation associated with a small, talc-alkaline porphyry intrusion of Caledonian age is described. Geological mapping has delineated a small stock of biotite-feldspar 2 porphyry, 0.25 km in area, intruded into a sequence of Dalradian schists and quartzites with inter-bedded epidiorites. Disseminated sulphides occur within the porphyry and the hornfelsed epidiorite but do not normally , exceed 3% of the rock by volume. Assays of both rock types obtained maximum levels of 0.24% Cu. Hydrothermal alteration is prominent within the porphyry, with the widespread development of sericite and kaolinite. Subsequent faulting apparently exerted some control on the present limits of alteration and mineralisation. Several small strata-bound lenses of massive sulphide within the metasediments were recorded but were not investigated in detail. Geochemical rock-sampling delineated a strong arcuate copper anomaly over the northern epidiorite/porphyry contact and a more subdued anomaly along the southern faulted contact. Molybdenum shows a similar distribution but is more closely confined to the porphyry. Overburden sampling demonstrated that little or no metal dispersion occurred within the overlying till, and stream sediment sampling of the catchment area showed that concentrations of copper decrease to background levels within 1 km of the intrusion. 1 Induced polarisation (IP) surveys produced a clearly defined grouping of chargeability anomalies around the edges of the porphyry stock but these showed no increase in magnitude at depth. Above background chargeability values recorded over the centre of the intrusion increase markedly towards the margins , possibly reflecting a pyritic halo. Most resistivity 'lows' coincide with fault zones and the results of a total intensity magnetometer 1 survey showed that, while most magnetic anomalies occur over epidiorites, I significant anomalies are also produced in the vicinity of fault zones. The results of the geological, petrographic, geochemical and I geophysical studies demonstrate the presence of several features : characteristic of 'porphyry copper style' mineralisation but the small 1 surface area and low grade of the deposit, combined with a lack of encouraging geophysical responses at depth, suggest that there is little II chance of discovering an economic orebody by exploratory drilling

Improving data preparation for the application of process mining

Immersed in what is already known as the fourth industrial revolution, automation and data exchange are taking on a particularly relevant role in complex environments, such as industrial manufacturing environments or logistics. This digitisation and transition to the Industry 4.0 paradigm is causing experts to start analysing business processes from other perspectives. Consequently, where management and business intelligence used to dominate, process mining appears as a link, trying to build a bridge between both disciplines to unite and improve them. This new perspective on process analysis helps to improve strategic decision making and competitive capabilities. Process mining brings together data and process perspectives in a single discipline that covers the entire spectrum of process management. Through process mining, and based on observations of their actual operations, organisations can understand the state of their operations, detect deviations, and improve their performance based on what they observe. In this way, process mining is an ally, occupying a large part of current academic and industrial research. However, although this discipline is receiving more and more attention, it presents severe application problems when it is implemented in real environments. The variety of input data in terms of form, content, semantics, and levels of abstraction makes the execution of process mining tasks in industry an iterative, tedious, and manual process, requiring multidisciplinary experts with extensive knowledge of the domain, process management, and data processing. Currently, although there are numerous academic proposals, there are no industrial solutions capable of automating these tasks. For this reason, in this thesis by compendium we address the problem of improving business processes in complex environments thanks to the study of the state-of-the-art and a set of proposals that improve relevant aspects in the life cycle of processes, from the creation of logs, log preparation, process quality assessment, and improvement of business processes. Firstly, for this thesis, a systematic study of the literature was carried out in order to gain an in-depth knowledge of the state-of-the-art in this field, as well as the different challenges faced by this discipline. This in-depth analysis has allowed us to detect a number of challenges that have not been addressed or received insufficient attention, of which three have been selected and presented as the objectives of this thesis. The first challenge is related to the assessment of the quality of input data, known as event logs, since the requeriment of the application of techniques for improving the event log must be based on the level of quality of the initial data, which is why this thesis presents a methodology and a set of metrics that support the expert in selecting which technique to apply to the data according to the quality estimation at each moment, another challenge obtained as a result of our analysis of the literature. Likewise, the use of a set of metrics to evaluate the quality of the resulting process models is also proposed, with the aim of assessing whether improvement in the quality of the input data has a direct impact on the final results. The second challenge identified is the need to improve the input data used in the analysis of business processes. As in any data-driven discipline, the quality of the results strongly depends on the quality of the input data, so the second challenge to be addressed is the improvement of the preparation of event logs. The contribution in this area is the application of natural language processing techniques to relabel activities from textual descriptions of process activities, as well as the application of clustering techniques to help simplify the results, generating more understandable models from a human point of view. Finally, the third challenge detected is related to the process optimisation, so we contribute with an approach for the optimisation of resources associated with business processes, which, through the inclusion of decision-making in the creation of flexible processes, enables significant cost reductions. Furthermore, all the proposals made in this thesis are validated and designed in collaboration with experts from different fields of industry and have been evaluated through real case studies in public and private projects in collaboration with the aeronautical industry and the logistics sector

Analysis of CO₂ leakage through "low-permeability" faults from natural reservoirs in the Colorado Plateau, southern Utah

The numerous CO2 reservoirs in the Colorado Plateau region of the United States are natural analogues for potential geologic CO2 sequestration repositories. To better understand the risk of leakage from reservoirs used for long-term underground CO2 storage, we examine evidence for CO2 migration along two normal faults from a reservoir in east-central Utah. CO2 -charged springs, geysers, and a hydrocarbon seep are localised along these faults. These include natural springs that have been active for long periods of time, and springs that were induced by recent drilling. The CO2 -charged spring waters have deposited travertine mounds and carbonate veins. The faults cut siltstones, shales, and sandstones and the fault rocks are fine-grained, clay-rich gouge, generally thought to be barriers to fluid flow. The geologic and geochemical data are consistent with these faults being conduits for CO2 to the surface. Consequently, the injection of CO2 into faulted geologic reservoirs, including faults with clay gouge, must be carefully designed and monitored to avoid slow seepage or fast rupture to the biosphere

Analysis of ERTS-1 linear features in New York State

The author has identified the following significant results. All ERTS-1 linears confirmed to date have topographic expression although they may appear as featureless tonal linears on the imagery. A bias is unavoidably introduced against any linears which may parallel raster lines, lithological trends, or the azimuth of solar illumination. Ground study of ERTS-1 topographic lineaments in the Adirondacks indicates: outcrops along linears are even more rare than expected, fault breccias are found along some NNE lineaments, chloritization and slickensiding without brecciation characterize one EW lineament whereas closely-spaced jointing plus a zone of plastic shear define another. Field work in the Catskills suggests that the prominent new NNE lineaments may be surface manifestations of normal faulting in the basement, and that it may become possible to map major joint sets over extensive plateau regions directly on the imagery. Fall and winter images each display some unique linears, and long linears on the fall image commonly appear as aligned segments on the winter scene. A computer-processed color composite image permitted the extraction or additional information on the shaded side of mountains

Geologic Modeling of Magnetic Data for Cypress Island, Washington

Cypress Island, Washington is composed of three distinct rock units separated by two major faults with east-west trending surface traces. The rock units are from south to north; an ultramafic unit with varyingly serpentinized harzburgite, a volcanic/ sedimentary unit composed of basalt, numerous pelagic sediments and serpentine, and a greywacke unit. The orientations of the fault contacts at depth are not interpretable by surface data. This study compiled magnetic data from several sources to model subsurface geologic aspects of the island. Magnetic anomaly profiles were created from the data, then analyzed and modeled using the computer program GM-SYS. The primary goal of this study was to model the orientation of the fault separating the ultramafic unit from the volcanic/sedimentary unit. The results indicate that the fault has a high-angle south dipping orientation. Data collected also indicate unexpected large quantities of subsurface magnetic material in the volcanic/sedimentary unit. Due to the presence of small amounts of serpentine found as outcrop in this unit, these subsurface magnetic bodies are interpreted to be serpentine as well. The distribution and quantity of this serpentine suggests that the sedimentary/volcanic unit is a serpentine melange