http://repository.iiitd.edu.in/xmlui/handle/123456789/708 2026-04-10T22:47:17Z 2026-04-10T22:47:17Z Gupta, Monika Jalote, Pankaj (Advisor) Serebrenik, Alexander (Advisor) http://repository.iiitd.edu.in/xmlui/handle/123456789/945 2021-12-02T22:00:17Z 2019-12-01T00:00:00Z

Improving software maintenance ticket resolution using process mining Gupta, Monika; Jalote, Pankaj (Advisor); Serebrenik, Alexander (Advisor) Software maintenance refers to the modification of software product after delivery and is required to correct faults, to improve the performance or other attributes, or to adapt the product to a modified environment. It is a crucial activity in the software industry and consumes a major portion of the expenditure on software. It is known that the performance of an organization can be improved by improving the process. Therefore, given the importance and cost involved, there is need to continuously improve the software maintenance process. This thesis focuses on analyzing and improving the software maintenance ticket resolution process by exploring novel applications of process mining. We decided to study the ticket resolution process as it is an important part of a software maintenance process. Process mining consists of mining event logs generated from business process execution supported by information systems. To identify the potential opportunities for improvement in software process management by mining data repositories, we first conducted qualitative interviews and surveys of more than 40 managers in a large global IT company. The survey provided us with a list of more than 10 maintenance process challenges encountered by practitioners, and benefits that might accrue by addressing them. This thesis addressed a few of the identified challenges pertaining to the software maintenance ticket resolution process. We studied different types of software maintenance tickets, that is, software bug tickets and IT support tickets. As identified from the survey, there is a need to analyze the data generated during the ticket resolution process to capture process reality and identify the process inefficiencies. Hence, we proposed a framework to analyze the data for ticket resolution process from diverse perspectives, by applying process mining techniques. Using the proposed framework, we discovered the process model that captured the control flow, timing and frequency information about events. We then studied inefficiencies such as self-loops, back-forth, ticket reopen, timing issues, delay due to user input requests, and effort consumption. We also analyzed the degree of conformance between the designed and the runtime (discovered) process model. The data-driven insights helped to make process improvement decisions. For example, using the proposed framework on IT support ticket data for a large global IT company we found that around 57% of the tickets had user input requests in the life cycle, causing significant delays in user-experienced resolution time. Therefore, we proposed a machine learning based-system that preempts a user at the time of ticket submission with an average accuracy of around 94% to provide additional information that the analyst is likely to ask, thus mitigating delays due to later user input requests. XIII Also, we explored unstructured data generated during process execution to derive insights that could not be obtained solely from structured data (event logs). To achieve this, we extracted topical phrases (keyphrases) from the unstructured data using an unsupervised graph-based approach. The keyphrases were then integrated into the event log, which got reflected in the discovered process model. To resolve a ticket, some code changes were made, which led to anomalies such as regression bugs. We aimed to detect whether ticket resolution caused some anomalous behavior so as to reduce the post-release bugs, one of the important challenges identified from the survey. To achieve this, we proposed an approach to discover an execution behavior model for the deployed and the new version using the execution logs that is, runtime print statements. Differences between the two models were then identified, which allowed programmers to detect anomalous behavior changes, that is, not consistent with code changes thereby identifying potential bugs that might have been introduced during code change. We applied the proposed framework and solution approaches on a series of case studies on data sets of commercial and open source projects. Through the aforementioned contributions, we explored the potential of applying process mining using various data sources to improve various aspects of the software maintenance ticket resolution process. Such analysis usually focuses on identifying the inefficiencies, but as we observed in the thesis, it can also lead to automation opportunities to make the ticket resolution process more efficient.

2019-12-01T00:00:00Z N., Nalla Anandakumar Sanadhya, Somitra Kumar (Advisor) Hashmi, Mohammad S. (Advisor) http://repository.iiitd.edu.in/xmlui/handle/123456789/798 2020-01-28T22:00:15Z 2019-09-01T00:00:00Z

Design, implementation and analysis of efficient hardware-based security primitives N., Nalla Anandakumar; Sanadhya, Somitra Kumar (Advisor); Hashmi, Mohammad S. (Advisor) Internet of Things (IoT) is a vast and rapidly growing technology right now in the world of innovation. Billions of new electronic devices are going to be connected to the internet in wide-ranging applications. With this massive increase in adoption and utilization of new technology, security vulnerabilities are growing exponentially as well. Traditionally, conventional cryptographic primitives are used in order to provide security of these devices. The security of the cryptographic protection relies on the secrecy of the key. Typically, secret keys, which are used as device identification (IDs), are stored in non-volatile memories (NVMs), and combine cryptographic primitives to implement information encryption and authentication. However, through such traditional technique, secret keys are vulnerable to various kinds of attacks and can be easily obtained or cloned. Further, maintaining such secrets in NVMs is difficult and expensive. In addition, random key generation and key exchange are also very challenging in secure IoT applications. Physically Unclonable Function (PUF) promises to be a critical hardware security primitive to provide an alternative method to create unique signatures (IDs) from complex physical characteristics of ICs rather than storing the IDs in non-volatile memories. Eventually these IDs can be used to authenticate devices and also to generate secret keys for cryptographic functions. A True Random Number generator (TRNG) is another important hardware security primitive that generates high entropy random numbers (keys) from a physical process for use in key exchange/agreement, encryption, and digital signature, etc. The IoT infrastructure adopts a large number of these hardware-based security primitives in order to securely exchange data in an effective and resource efficient manner. Furthermore, one of the major requirements of PUF and TRNG intended for IoT applications is that the device area must be efficiently utilized. Unfortunately, the huge area consumption of many PUF and TRNG implementations on Field-Programmable Gate Arrays (FPGAs) made them infeasible in IoT environments. Therefore, we undertake the study and development of new techniques to design, develop and implement highly efficient PUFs and TRNG for FPGAs in the context of IoT applications in this thesis. In the first part of this thesis, we study different techniques for improving performance characteristics of PUFs. In this context, we carry out the design, development, implementation and evaluation of four major types of PUFs has for IoT security. These PUFs fall in three categories: memory based, delay based or hybrid PUFs. The first design we study is RS-Latch based which is a memory based PUF. Next two designs are Ring oscillator and Arbiter based, and fall in the category of delay based PUF. The fourth design is a hybrid of RS Latch and Arbiter PUF designs. All the four designs have been thoroughly tested on FPGA devices. The enhancement in performance of the new designs is achieved through the incorporation of various novel techniques. Performance metrics of these designs have been presented and compared to the state of the art PUFs. It has also been shown that the proposed designs yield the most area-efficient conventional and hybrid PUFs reported so far. Moreover, the proposed PUFs are resistant to temperature, supply voltage, and correlated process variations making them attractive for IoT applications. In the second part of this thesis, we design and develop a ring oscillators based true random number generation on FPGA. The quality of generated true random bits can be improved by employing different new techniques. Subsequently experimental evaluation and comparisons with existing techniques are presented. Further, our proposed implementation provides a very good area-throughput trade-off and high entropy rate of the produced output bits when compared to the existing state-of-the-art. Lastly, in the third part of this work, we focus on efficient FPGA implementation of elliptic curve based authenticated key agreement protocol for IoT devices using PUF and TRNG. In this context, we design and develop a novel hardware architecture for Binary Edwards Curve (BEC) point multiplication. Subsequently, an FPGA design of elliptic curve based key agreement protocol (ECMQV) using PUF and TRNG is presented. The obtained implementation results show that the proposed architecture yields a better performance when compared to the existing state-of-the-art.

2019-09-01T00:00:00Z Maggu, Jyoti Majumdar, Angshul (Advisor) http://repository.iiitd.edu.in/xmlui/handle/123456789/797 2020-01-07T22:00:19Z 2019-12-01T00:00:00Z

Deep transform learning Maggu, Jyoti; Majumdar, Angshul (Advisor) Conventional dictionary learning is a synthesis formulation; it learns a dictionary to generate/synthesize the data from the learned coefficients. Transform learning is its analysis equivalent. The transform analyzes the data to generate the coefficients. Dictionary learning had been popular in both signal processing and machine learning communities. However, transform learning is largely unknown outside the signal processing research community. So far, transform learning has been primarily used for solving inverse problems. The objective of the thesis is to build a completely new machine learning framework out of transform learning. It has already been shown how the basic transform learning has been used as an unsupervised feature extraction tool. This work aims at proposing a supervised version of transform learning with a plug-and-play approach. The supervised version is general enough to perform classification without the need for any external classifier. The kernelized version of supervised transform learning and stochastic regularization on transform learning are also proposed. Based on the proposed supervised transform learning framework, problems on computer vision, bioinformatics, hyperspectral image classification, and arrhythmia classification are solved. This work also focuses on an unsupervised greedy deep transform learning problem, where each of the layers was solved separately. This was a solution for unsupervised feature extraction using deep transform learning. But the greedy solution for deep transform learning was sub-optimal. Then work has been done on proposing an optimal solution to learn all the layers jointly. It was used to solve classification, clustering and inverse problems. Another problem discussed in this work is the supervised version of deep transform learning. The supervised version is general enough to perform single-label classification and multi-label classification. Proposed supervised deep transform learning for multi-label classification has been used for solving a practical problem of non-intrusive load monitoring. Another contribution of this work is to propose a deeply transformed subspace clustering framework. In this work, two techniques are introduced: transformed locally linear manifold clustering and transformed sparse subspace clustering. Next, a deeper architecture for the same is proposed. Then, the idea of convolutional transform learning is introduced. Here, a set of independent convolutional filters are learned that operate on the images to produce representations (one corresponding to each filter). The kernels learned from this method have a close relationship with that of convolutional neural networks. Finally, a semi-coupled transform learning framework is introduced. Given training data in two domains (source and target), it learns a transform in each of the domains such that the corresponding coefficients are (linearly) mapped from the source to the target. Since the mapping is in one direction (source to target) but not the other way round, It is called semi-coupled. This work is the analysis equivalent of (semi) coupled dictionary learning.

2019-12-01T00:00:00Z Jain, Milan Singh, Amarjeet (Advisor) Chandan, Vikas (Advisor) http://repository.iiitd.edu.in/xmlui/handle/123456789/795 2019-12-31T03:32:21Z 2019-03-01T00:00:00Z

Data-driven thermostats : for feedback, comfort, and reliability Jain, Milan; Singh, Amarjeet (Advisor); Chandan, Vikas (Advisor) In buildings, air conditioning consumes a significant proportion of the aggregate electricity bill. Specifically, in residential and small-scale commercial buildings, people prefer to use room-level ACs (AC stands for Air Conditioning in this thesis) with an in-built thermostat. As thermostat settings conceptually govern AC energy consumption and user comfort; over the years, researchers spent a significant amount of time and effort in enhancing thermostats to ensure optimal usage of ACs. By analyzing occupants' behaviour, today, smart thermostats, with multiple sensory abilities, can automatically adjust the set temperature to maximize both - energy savings and user comfort. While thermostats are smart and ubiquitous, they often rely on occupants' dynamic schedule for the automated control of the set-point temperature. For a typical home, where everyone follows a particular routine, any deviation in daily schedule often leads to user discomfort. In addition to that, smart thermostats neither consider spatial variations across the buildings, nor temporal variations, such as climate change, while changing the set temperature. Subsequently, even today, the expensive thermostats are confined to automated temperature variation, with a limited scope of boosting the energy savings and enhancing the user comfort. In this dissertation, we address these concerns and introduce Data-Driven Thermostats to make AC experience efficient and comfortable for the users. First, we propose PACMAN that monitors room temperature to ensure tenants' participation during AC usage by providing actionable energy-feedback. Next, we recommend a Comfort-Energy Trade-off (CET) knob, realized through an optimization framework, to allow users to balance their comfort and savings without worrying about the right set temperature. Our study indicates that such a knob can reduce residents’ discomfort by 23% and save 26% energy. Third, we investigate the impact of occupancy prediction errors on occupants' comfort and total energy consumption of a building. Finally, we propose Greina - to continuously monitor the readily available ambient information from the thermostat and timely report refrigerant leaks through the coils (or valves) of a refrigeration unit. Such leaks waste significant energy, risk occupants' health, and affect user comfort. Our methods are novel, scalable, and more effective than the state-of-the-art smart thermostats.

2019-03-01T00:00:00Z