Trace visualization within the Software City metaphor: Controlled experiments on program comprehension

Software application becomes critical tool in a business operation today. Generally there is no management staff in the business operation can tolerate the software application downtime beyond the Service Level Agreement (SLA). The time duration spent on root cause analysis activity of software application error carries crucial impact to the business transaction. This is because business operation cannot afford if the software application downtime gets prolonged. Especially an enterprise level of software application is involving multiple tiers. There will be at least one or even more log files at each tier. Of course the root cause analysis activity will easily get prolonged as more than one log file are required for analysis activity. The research is not only to mitigate the time duration prolonging on root cause analysis activity. It is also to increase the accuracy of identifying the actual root cause during analysis activity. This is a necessity to have a proposed logic model sitting at the logic tier. The logic model can analyze the required log files, and then identify the root cause without creating any interruption to the existing software application executing in the production environment. Therefore, this is the strong reason for a Prescriptive Analytical Logic Model (PAL) incorporated with Analytic Hierarchy Process (AHP) proposed. The proposed logic model will contribute a new knowledge in the area of log file analysis. It aligns with the two research objectives, (1) to shorten the total time spent on root cause analysis activity by adopting the decision making process and technique of AHP, (2) to reduce the frequency of attempt by increasing the accuracy of identifying the actual root cause. Furthermore, this proposed logic model contributes the new knowledge to close the existing knowledge gap of software application root cause analysis using AHP.

Development of a novel methodology for root cause analysis and selection of maintenance strategy for a thermal power plant: A data exploration approach

The paper reports the investigation on the root cause of source-drain leakage in bulk FinFET devices. While the failing device was readily isolated by nanoprobing technique and the electrical analysis pinpointed the potential defect location inside the Fin channel, the identification of physical root cause went through extreme challenges imposed by the tiny-sized device and the unique FinFET 3D architecture. The initial TEM analysis was misled by the projection of a species in the lamella surface and thus could not explain the electrical data. Careful analysis on the device structure was able to identify the origin of the species and led to the discovery of the actual root cause. This paper will provide the analysis details leading to the findings, and highlight the role of electrical understanding in not only providing guidance for physical analysis but also revealing the true root cause of failure in FinFET devices.

Cloud-based services are surging into popularity in recent years. However, outages, i.e., severe incidents that always impact multiple services, can dramatically affect user experience and incur severe economic losses. Locating the root-cause service, i.e., the service that contains the root cause of the outage, is a crucial step to mitigate the impact of the outage. In current industrial practice, this is generally performed in a bootstrap manner and largely depends on human efforts: the service that directly causes the outage is identified first, and the suspected root cause is traced back manually from service to service during diagnosis until the actual root cause is found. Unfortunately, production cloud systems typically contain a large number of interdependent services. Such a manual root cause analysis is often time-consuming and labor-intensive. In this work, we propose COT, the first outage triage approach that considers the global view of service correlations. COT mines the correlations among services from outage diagnosis data. After learning from historical outages, COT can infer the root cause of emerging ones accurately. We implement COT and evaluate it on a real-world dataset containing one year of data collected from Microsoft Azure, one of the representative cloud computing platforms in the world. Our experimental results show that COT can reach a triage accuracy of 82.1%-83.5%, which outperforms the state-of-the-art triage approach by 28.0%-29.7%.

The Theory of Constraints was used as a method in a root cause analysis (RCA) to identify system performance bottlenecks, wherein the constraint with the strongest correlation with system performance is identified as the bottleneck. In the present study, this method was applied in a large district cooling plant to diagnose steam absorption chillers’ performance anomalies including performance deterioration, spikes, large variation, etc. Many root causes were identified through a ten months’ continuous diagnosis, such as drastic reduction in chilled water flow rate, faulty steam control valve, cooling demand reduction, etc. More than 30 anomalies were diagnosed and more than 80% diagnosis accuracy was achieved. The diagnosis results were verified by the plant operator, which proved the effectiveness of the Theory of Constraints as a method for root cause analysis in diagnosing anomalies in district cooling plant operations. It was however discovered that even though the operational component identification accuracy was high, the determination of the actual root cause still presents lots of challenges, due to the large set of domain knowledge and operation conditions required. A knowledge-based supporting tool may be needed as part of the RCA to facilitate a speedy and complete analysis to enable these anomalies to be resolved.

An important step in root cause analysis is the identification of the time when process first changed. The time when a disturbance first manifested itself into the process is referred to as change point. Identification of the change point could help process engineer to perform root cause analysis effectively. In this paper, an estimator for the change point of a normal process mean using artificial neural network (ANN) is proposed. Five patterns of change namely single step, linear trend, systematic, cyclic, and mixture are studied. Whenever possible, results are compared numerically to the results obtained by other methods proposed by different researchers. First the type of change to be recognized by an ANN-based pattern recognizer is identified and then the change point in the process mean is estimated. Results indicate satisfactory performance for the proposed method that could be used as an effective method for root cause analysis by process engineer.

One of the main goals of predictive maintenance is to accurately classify the temporal trends as early as possible, detect faulty states and pinpoint the root cause of the fault. Undoubtedly, neither late nor early maintenance is desirable and incurs additional operating costs; however, early identification of faulty trends and scheduling on-time maintenance is crucial to smooth machinery operation. Various data-driven techniques have been used to identify faults; nevertheless, many of these techniques fail to perform when faced with missing values at run time or lack any explanation on the root cause of the fault. The present work offers a comprehensive study on different techniques used for fault type classification and compares their performance in identifying the mode of operation for the PHME21 dataset. We also evaluate the robustness of such classifiers against missing values. This study shows that tree-based techniques are best suited to perform root cause analysis for each faulty state and establish rules for faulty conditions.

After three decades of false hopes and failures, a pipeline of therapeutic drugs that target the actual root cause of Alzheimer's disease (AD) is now available. Challenging the old paradigm that focused on β-amyloid peptide (Aβ) aggregation in amyloid plaques, these compounds are designed to prevent the neurotoxicity of Aβ oligomers that form Ca2+ permeable pores in the membranes of brain cells. By triggering an intracellular Ca2+ overdose, Aβ oligomers induce a cascade of neurotoxic events including oxidative stress, tau hyperphosphorylation, and neuronal loss. Targeting any post-Ca2+ entry steps (e.g., tau) will not address the root cause of the disease. Thus, preventing Aβ oligomers formation and/or blocking their toxicity is by essence the best approach to stop any progression of AD. Three categories of anti-oligomer compounds are already available: antibodies, synthetic peptides, and small drugs. Independent in silico-based designs of a peptide (AmyP53) and a monoclonal antibody (PMN310) converged to identify a histidine motif (H13/H14) that is critical for oligomer neutralization. This "histidine trick" can be viewed as the Achilles' heel of Aβ in the fight against AD. Moreover, lipid rafts and especially gangliosides play a critical role in the formation and toxicity of Aβ oligomers. Recognizing AD as a membrane disorder and gangliosides as the key anti-oligomer targets will provide innovative opportunities to find an efficient cure. A "full efficient" solution would also need to be affordable to anyone, as the number of patients has been following an exponential increase, affecting every part of the globe.

This paper places a strong emphasis on the importance of applying Systematic Problem Solving approach and use of appropriate FA methods and tools to understand the “real” failure root cause. A case of wafer center cluster RAM fail due to systematic missing Cu was studied. It was through a strong “inquisitive” mindset coupled with deep dive problem solving that lead to uncover the actual root cause of large Cu voids. The missing Cu was due to large Cu void induced by galvanic effects from the faster removal rate during Cu CMP and subsequently resulted in missing Cu. This highlights that the FA analyst’s mission is not simply to find defects but also play a catalyst role in root cause/failure mechanism understanding by providing supporting FA evidence (electrically/ physically) to Fab.

Blood and Body Fluid Exposures (BFFEs) involve a complex sequence of events combining technical, human and organizational factors. Performing root cause analysis (RCA) of these events is promoted to improve safety. We conducted a RCA of a BBFE in a radiology unit using a recently developed French method called Orion.

Problems and their mitigation in system and software architecting

On curved tracks, high-pitch noise pollution can often be a considerable concern of rail asset owners, commuters, and people living or working along the rail corridor. Inevitably, wheel/rail interface can cause a traveling source of sound and vibration, which spread over a long distance of rail network. The sound and vibration can be in various forms and spectra. The undesirable sound and vibration on curves is often called ‘noise,’ includes flanging and squealing noises. This paper focuses on the squeal noise phenomena on curved tracks located in urban environments. It highlights the effect of curve radii on lateral track dynamics. It is important to note that rail freight curve noises, especially for curve squeals, can be observed almost everywhere and every type of track structures. The most pressing noise appears at sharper curved tracks where excessive lateral wheel/rail dynamics resonate with falling friction states, generating a tonal noise problem, so-call ‘squeal’. Many researchers have carried out measurements and simulations to understand the actual root causes of the squeal noise. Most researchers believe that wheel resonance over falling friction is the main cause, whilst a few others think that dynamic mode coupling of wheel and rail may also cause the squeal. Therefore, this paper is devoted to systems thinking the approach and dynamic assessment in resolving railway curve noise problems. The simulations of railway tracks with different curve radii will be carried out to develop state-of-the-art understanding into lateral track dynamics, including rail dynamics, cant dynamics, gauge dynamics and overall track responses.

Coiled tubing has been widely used worldwide to perform perforating and zonal isolation operation due to the ability in intervening highly deviated and long section of horizontal wells under live condition, where slickline and E-line have difficulties. This paper presents case history of coiled tubing perforating and zonal isolation evolution in infill well at Resak field, one of the gas field operated by Malaysia National E&P Company, Petronas Carigali Sdn Bhd. Since the beginning of Resak Field production, coiled tubing has been used to perforate numbers of infill wells with low success ratio. The reservoir characteristics with high formation pressure and BHT followed by high CO2, H2S production and improper well clean up contributed in the increase of operational risks and challenges. Several failures reported in the past was carefully analyzed to determine the actual root cause prior to coming up with the proper job design and operational procedures. CTU with 1.5" CT reel was used to convey 140 ft of 2-7/8" gun due to crane and platform deck loading limitation. The small CT size and large gun conveyance required extra precaution since the presence of gun shock during perforation may affect the CT integrity. Therefore supporting perforating software was run in advanced to optimize gun selection where both operational risks and production objectives were taken into consideration. Coiled tubing forces simulation was also used to determine the ability of the gun assemblies to be safely conveyed into the target sands. In addition to that, one of the unique features from the gun assemblies was the selection of electronic firing head over a conventional or standard drop ball mechanism. This tool utilized coded sequence created from variation of surface pump rate to generate its firing command, providing reliable gun activation where as the drop ball mechanism in the past was found to have some operational difficulties. Lastly, this paper also mentions the proper selection of zonal isolation method that can withstand the wellbore condition, providing adequate isolation whilst able to be retrieved for future production requirements. The result of those engineering studies managed to overcome the operational failures in the past and at the same time was able to meet the production objective. Project Result After performing the coiled tubing operation by isolating J10.2 with inflatable bridge plug and perforating J10.1 with deep penetration gun, the well managed to be produced at 22.7 MMSCF/D & stabilized at 48 MMCSF/D (23 % choke size) with total gain of 41MMSCF/D which was higher than the expected gain of 35 MMSCF/day (Figure 1). Currently the well is the highest producing well in Resak field with stable production at 49.8 MMSCCF/D. Figure 1Production Data before and After CT Perforating

On a 45nm logic product that suffered special pattern function failure, electrical failure analysis identified the failure as soft failure and localized the failure addresses. PVC (Passive Voltage Contrast) was used to narrow down suspected process steps to M1 or below layers, FIB (Focus Ion Beam) and TEM (Transmission Electronic Microscope) approaches were used to identify the actual root cause and failure mechanism.

The Radiation Incident Safety Committee: Supporting Cancer Programs in the Delivery of High Quality Radiation Treatment