Machine Learning-Based Bug Handling in Large-Scale Software Development PDF Download

Author: Leif Jonsson
Publisher: Linköping University Electronic Press
ISBN: 9176853063
Category :
Languages : en
Pages : 120

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Book Description
This thesis investigates the possibilities of automating parts of the bug handling process in large-scale software development organizations. The bug handling process is a large part of the mostly manual, and very costly, maintenance of software systems. Automating parts of this time consuming and very laborious process could save large amounts of time and effort wasted on dealing with bug reports. In this thesis we focus on two aspects of the bug handling process, bug assignment and fault localization. Bug assignment is the process of assigning a newly registered bug report to a design team or developer. Fault localization is the process of finding where in a software architecture the fault causing the bug report should be solved. The main reason these tasks are not automated is that they are considered hard to automate, requiring human expertise and creativity. This thesis examines the possi- bility of using machine learning techniques for automating at least parts of these processes. We call these automated techniques Automated Bug Assignment (ABA) and Automatic Fault Localization (AFL), respectively. We treat both of these problems as classification problems. In ABA, the classes are the design teams in the development organization. In AFL, the classes consist of the software components in the software architecture. We focus on a high level fault localization that it is suitable to integrate into the initial support flow of large software development organizations. The thesis consists of six papers that investigate different aspects of the AFL and ABA problems. The first two papers are empirical and exploratory in nature, examining the ABA problem using existing machine learning techniques but introducing ensembles into the ABA context. In the first paper we show that, like in many other contexts, ensembles such as the stacked generalizer (or stacking) improves classification accuracy compared to individual classifiers when evaluated using cross fold validation. The second paper thor- oughly explore many aspects such as training set size, age of bug reports and different types of evaluation of the ABA problem in the context of stacking. The second paper also expands upon the first paper in that the number of industry bug reports, roughly 50,000, from two large-scale industry software development contexts. It is still as far as we are aware, the largest study on real industry data on this topic to this date. The third and sixth papers, are theoretical, improving inference in a now classic machine learning tech- nique for topic modeling called Latent Dirichlet Allocation (LDA). We show that, unlike the currently dominating approximate approaches, we can do parallel inference in the LDA model with a mathematically correct algorithm, without sacrificing efficiency or speed. The approaches are evaluated on standard research datasets, measuring various aspects such as sampling efficiency and execution time. Paper four, also theoretical, then builds upon the LDA model and introduces a novel supervised Bayesian classification model that we call DOLDA. The DOLDA model deals with both textual content and, structured numeric, and nominal inputs in the same model. The approach is evaluated on a new data set extracted from IMDb which have the structure of containing both nominal and textual data. The model is evaluated using two approaches. First, by accuracy, using cross fold validation. Second, by comparing the simplicity of the final model with that of other approaches. In paper five we empirically study the performance, in terms of prediction accuracy, of the DOLDA model applied to the AFL problem. The DOLDA model was designed with the AFL problem in mind, since it has the exact structure of a mix of nominal and numeric inputs in combination with unstructured text. We show that our DOLDA model exhibits many nice properties, among others, interpretability, that the research community has iden- tified as missing in current models for AFL.

Author: Leif Jonsson
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Book Description
This thesis investigates the possibilities of automating parts of the bug handling process in large-scale software development organizations. The bug handling process is a large part of the mostly manual, and very costly, maintenance of software systems. Automating parts of this time consuming and very laborious process could save large amounts of time and effort wasted on dealing with bug reports. In this thesis we focus on two aspects of the bug handling process, bug assignment and fault localization. Bug assignment is the process of assigning a newly registered bug report to a design team or developer. Fault localization is the process of finding where in a software architecture the fault causing the bug report should be solved. The main reason these tasks are not automated is that they are considered hard to automate, requiring human expertise and creativity. This thesis examines the possi- bility of using machine learning techniques for automating at least parts of these processes. We call these automated techniques Automated Bug Assignment (ABA) and Automatic Fault Localization (AFL), respectively. We treat both of these problems as classification problems. In ABA, the classes are the design teams in the development organization. In AFL, the classes consist of the software components in the software architecture. We focus on a high level fault localization that it is suitable to integrate into the initial support flow of large software development organizations. The thesis consists of six papers that investigate different aspects of the AFL and ABA problems. The first two papers are empirical and exploratory in nature, examining the ABA problem using existing machine learning techniques but introducing ensembles into the ABA context. In the first paper we show that, like in many other contexts, ensembles such as the stacked generalizer (or stacking) improves classification accuracy compared to individual classifiers when evaluated using cross fold validation. The second paper thor- oughly explore many aspects such as training set size, age of bug reports and different types of evaluation of the ABA problem in the context of stacking. The second paper also expands upon the first paper in that the number of industry bug reports, roughly 50,000, from two large-scale industry software development contexts. It is still as far as we are aware, the largest study on real industry data on this topic to this date. The third and sixth papers, are theoretical, improving inference in a now classic machine learning tech- nique for topic modeling called Latent Dirichlet Allocation (LDA). We show that, unlike the currently dominating approximate approaches, we can do parallel inference in the LDA model with a mathematically correct algorithm, without sacrificing efficiency or speed. The approaches are evaluated on standard research datasets, measuring various aspects such as sampling efficiency and execution time. Paper four, also theoretical, then builds upon the LDA model and introduces a novel supervised Bayesian classification model that we call DOLDA. The DOLDA model deals with both textual content and, structured numeric, and nominal inputs in the same model. The approach is evaluated on a new data set extracted from IMDb which have the structure of containing both nominal and textual data. The model is evaluated using two approaches. First, by accuracy, using cross fold validation. Second, by comparing the simplicity of the final model with that of other approaches. In paper five we empirically study the performance, in terms of prediction accuracy, of the DOLDA model applied to the AFL problem. The DOLDA model was designed with the AFL problem in mind, since it has the exact structure of a mix of nominal and numeric inputs in combination with unstructured text. We show that our DOLDA model exhibits many nice properties, among others, interpretability, that the research community has iden- tified as missing in current models for AFL.

Author: Michal Choraś
Publisher: Springer Nature
ISBN: 3030815234
Category : Technology & Engineering
Languages : en
Pages : 362

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Book Description
This book presents a collection of high-quality research papers accepted to multi-conference consisting of International Conference on Image Processing and Communications (IP&C 2021), International Conference on Computer Recognition Systems (CORES 2021), International Conference on Advanced Computer Systems (ACS 2021) held jointly in Bydgoszcz, Poland (virtually), in June 2021. The accepted papers address current computer science and computer systems-related technological challenges and solutions, as well as many practical applications and results. The first part of the book deals with advances in pattern recognition and classifiers, the second part is devoted to image processing and computer vision, while the third part addresses practical applications of computer recognition systems. Machine learning solutions for security and networks are tackled in part four of the book, while the last part collects papers on progress in advanced computer systems. We believe this book will be interesting for researchers and practitioners in many fields of computer science and IT applications.

Author: Arian Maghazeh
Publisher: Linköping University Electronic Press
ISBN: 9176851753
Category :
Languages : en
Pages : 62

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Book Description
Modern embedded systems deploy several hardware accelerators, in a heterogeneous manner, to deliver high-performance computing. Among such devices, graphics processing units (GPUs) have earned a prominent position by virtue of their immense computing power. However, a system design that relies on sheer throughput of GPUs is often incapable of satisfying the strict power- and time-related constraints faced by the embedded systems. This thesis presents several system-level software techniques to optimize the design of GPU-based embedded systems under various graphics and non-graphics applications. As compared to the conventional application-level optimizations, the system-wide view of our proposed techniques brings about several advantages: First, it allows for fully incorporating the limitations and requirements of the various system parts in the design process. Second, it can unveil optimization opportunities through exposing the information flow between the processing components. Third, the techniques are generally applicable to a wide range of applications with similar characteristics. In addition, multiple system-level techniques can be combined together or with application-level techniques to further improve the performance. We begin by studying some of the unique attributes of GPU-based embedded systems and discussing several factors that distinguish the design of these systems from that of the conventional high-end GPU-based systems. We then proceed to develop two techniques that address an important challenge in the design of GPU-based embedded systems from different perspectives. The challenge arises from the fact that GPUs require a large amount of workload to be present at runtime in order to deliver a high throughput. However, for some embedded applications, collecting large batches of input data requires an unacceptable waiting time, prompting a trade-off between throughput and latency. We also develop an optimization technique for GPU-based applications to address the memory bottleneck issue by utilizing the GPU L2 cache to shorten data access time. Moreover, in the area of graphics applications, and in particular with a focus on mobile games, we propose a power management scheme to reduce the GPU power consumption by dynamically adjusting the display resolution, while considering the user's visual perception at various resolutions. We also discuss the collective impact of the proposed techniques in tackling the design challenges of emerging complex systems. The proposed techniques are assessed by real-life experimentations on GPU-based hardware platforms, which demonstrate the superior performance of our approaches as compared to the state-of-the-art techniques.

Author: Management Association, Information Resources
Publisher: IGI Global
ISBN: 1799830179
Category : Computers
Languages : en
Pages : 2069

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Book Description
Programming has become a significant part of connecting theoretical development and scientific application computation. Computer programs and processes that take into account the goals and needs of the user meet with the greatest success, so it behooves software engineers to consider the human element inherent in every line of code they write. Research Anthology on Recent Trends, Tools, and Implications of Computer Programming is a vital reference source that examines the latest scholarly material on trends, techniques, and uses of various programming applications and examines the benefits and challenges of these computational developments. Highlighting a range of topics such as coding standards, software engineering, and computer systems development, this multi-volume book is ideally designed for programmers, computer scientists, software developers, analysts, security experts, IoT software programmers, computer and software engineers, students, professionals, and researchers.

Author: Anders Andersson
Publisher: Linköping University Electronic Press
ISBN: 9176850900
Category :
Languages : en
Pages : 42

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Book Description
Development of new functionality and smart systems for different types of vehicles is accelerating with the advent of new emerging technologies such as connected and autonomous vehicles. To ensure that these new systems and functions work as intended, flexible and credible evaluation tools are necessary. One example of this type of tool is a driving simulator, which can be used for testing new and existing vehicle concepts and driver support systems. When a driver in a driving simulator operates it in the same way as they would in actual traffic, you get a realistic evaluation of what you want to investigate. Two advantages of a driving simulator are (1.) that you can repeat the same situation several times over a short period of time, and (2.) you can study driver reactions during dangerous situations that could result in serious injuries if they occurred in the real world. An important component of a driving simulator is the vehicle model, i.e., the model that describes how the vehicle reacts to its surroundings and driver inputs. To increase the simulator realism or the computational performance, it is possible to divide the vehicle model into subsystems that run on different computers that are connected in a network. A subsystem can also be replaced with hardware using so-called hardware-in-the-loop simulation, and can then be connected to the rest of the vehicle model using a specified interface. The technique of dividing a model into smaller subsystems running on separate nodes that communicate through a network is called distributed simulation. This thesis investigates if and how a distributed simulator design might facilitate the maintenance and new development required for a driving simulator to be able to keep up with the increasing pace of vehicle development. For this purpose, three different distributed simulator solutions have been designed, built, and analyzed with the aim of constructing distributed simulators, including external hardware, where the simulation achieves the same degree of realism as with a traditional driving simulator. One of these simulator solutions has been used to create a parameterized powertrain model that can be configured to represent any of a number of different vehicles. Furthermore, the driver's driving task is combined with the powertrain model to monitor deviations. After the powertrain model was created, subsystems from a simulator solution and the powertrain model have been transferred to a Modelica environment. The goal is to create a framework for requirement testing that guarantees sufficient realism, also for a distributed driving simulation. The results show that the distributed simulators we have developed work well overall with satisfactory performance. It is important to manage the vehicle model and how it is connected to a distributed system. In the distributed driveline simulator setup, the network delays were so small that they could be ignored, i.e., they did not affect the driving experience. However, if one gradually increases the delays, a driver in the distributed simulator will change his/her behavior. The impact of communication latency on a distributed simulator also depends on the simulator application, where different usages of the simulator, i.e., different simulator studies, will have different demands. We believe that many simulator studies could be performed using a distributed setup. One issue is how modifications to the system affect the vehicle model and the desired behavior. This leads to the need for methodology for managing model requirements. In order to detect model deviations in the simulator environment, a monitoring aid has been implemented to help notify test managers when a model behaves strangely or is driven outside of its validated region. Since the availability of distributed laboratory equipment can be limited, the possibility of using Modelica (which is an equation-based and object-oriented programming language) for simulating subsystems is also examined. Implementation of the model in Modelica has also been extended with requirements management, and in this work a framework is proposed for automatically evaluating the model in a tool.

Author: Dietmar Winkler
Publisher: Springer
ISBN: 3030057674
Category : Computers
Languages : en
Pages : 176

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Book Description
This book constitutes the refereed proceedings of the 11th Software Quality Days Conference, SWQD 2019, held in Vienna, Austria, in January 2019. The Software Quality Days (SWQD) conference started in 2009 and has grown to the biggest conference on software quality in Europe with a strong community. The program of the SWQD conference is designed to encompass a stimulating mixture of practical presentations and new research topics in scientific presentations. The guiding conference topic of the SWQD 2019 is “The Complexity and Challenges of Software Engineering and Software Quality in the Cloud”. The 5 full papers and 3 short papers presented in this volume were carefully reviewed and selected from 17 submissions. The volume also contains 2 invited talks. The contributions were organized in topical sections named: multi-disciplinary systems and software engineering; software quality and process improvement; software testing; knowledge engineering and machine learning; source code analysis; and software maintenance.

Author: Måns Magnusson
Publisher: Linköping University Electronic Press
ISBN: 9176852881
Category :
Languages : en
Pages : 53

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Book Description
Probabilistic topic models have proven to be an extremely versatile class of mixed-membership models for discovering the thematic structure of text collections. There are many possible applications, covering a broad range of areas of study: technology, natural science, social science and the humanities. In this thesis, a new efficient parallel Markov Chain Monte Carlo inference algorithm is proposed for Bayesian inference in large topic models. The proposed methods scale well with the corpus size and can be used for other probabilistic topic models and other natural language processing applications. The proposed methods are fast, efficient, scalable, and will converge to the true posterior distribution. In addition, in this thesis a supervised topic model for high-dimensional text classification is also proposed, with emphasis on interpretable document prediction using the horseshoe shrinkage prior in supervised topic models. Finally, we develop a model and inference algorithm that can model agenda and framing of political speeches over time with a priori defined topics. We apply the approach to analyze the evolution of immigration discourse in the Swedish parliament by combining theory from political science and communication science with a probabilistic topic model. Probabilistiska ämnesmodeller (topic models) är en mångsidig klass av modeller för att estimera ämnessammansättningar i större corpusar. Applikationer finns i ett flertal vetenskapsområden som teknik, naturvetenskap, samhällsvetenskap och humaniora. I denna avhandling föreslås nya effektiva och parallella Markov Chain Monte Carlo algoritmer för Bayesianska ämnesmodeller. De föreslagna metoderna skalar väl med storleken på corpuset och kan användas för flera olika ämnesmodeller och liknande modeller inom språkteknologi. De föreslagna metoderna är snabba, effektiva, skalbara och konvergerar till den sanna posteriorfördelningen. Dessutom föreslås en ämnesmodell för högdimensionell textklassificering, med tonvikt på tolkningsbar dokumentklassificering genom att använda en kraftigt regulariserande priorifördelningar. Slutligen utvecklas en ämnesmodell för att analyzera "agenda" och "framing" för ett förutbestämt ämne. Med denna metod analyserar vi invandringsdiskursen i Sveriges Riksdag över tid, genom att kombinera teori från statsvetenskap, kommunikationsvetenskap och probabilistiska ämnesmodeller.

Author: Zeinab Ganjei
Publisher: Linköping University Electronic Press
ISBN: 9179296971
Category :
Languages : en
Pages : 192

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Book Description
There is currently an increasing demand for concurrent programs. Checking the correctness of concurrent programs is a complex task due to the interleavings of processes. Sometimes, violation of the correctness properties in such systems causes human or resource losses; therefore, it is crucial to check the correctness of such systems. Two main approaches to software analysis are testing and formal verification. Testing can help discover many bugs at a low cost. However, it cannot prove the correctness of a program. Formal verification, on the other hand, is the approach for proving program correctness. Model checking is a formal verification technique that is suitable for concurrent programs. It aims to automatically establish the correctness (expressed in terms of temporal properties) of a program through an exhaustive search of the behavior of the system. Model checking was initially introduced for the purpose of verifying finite‐state concurrent programs, and extending it to infinite‐state systems is an active research area. In this thesis, we focus on the formal verification of parameterized systems. That is, systems in which the number of executing processes is not bounded a priori. We provide fully-automatic and parameterized model checking techniques for establishing the correctness of safety properties for certain classes of concurrent programs. We provide an open‐source prototype for every technique and present our experimental results on several benchmarks. First, we address the problem of automatically checking safety properties for bounded as well as parameterized phaser programs. Phaser programs are concurrent programs that make use of the complex synchronization construct of Habanero Java phasers. For the bounded case, we establish the decidability of checking the violation of program assertions and the undecidability of checking deadlock‐freedom. For the parameterized case, we study different formulations of the verification problem and propose an exact procedure that is guaranteed to terminate for some reachability problems even in the presence of unbounded phases and arbitrarily many spawned processes. Second, we propose an approach for automatic verification of parameterized concurrent programs in which shared variables are manipulated by atomic transitions to count and synchronize the spawned processes. For this purpose, we introduce counting predicates that related counters that refer to the number of processes satisfying some given properties to the variables that are directly manipulated by the concurrent processes. We then combine existing works on the counter, predicate, and constrained monotonic abstraction and build a nested counterexample‐based refinement scheme to establish correctness. Third, we introduce Lazy Constrained Monotonic Abstraction for more efficient exploration of well‐structured abstractions of infinite‐state non‐monotonic systems. We propose several heuristics and assess the efficiency of the proposed technique by extensive experiments using our open‐source prototype. Lastly, we propose a sound but (in general) incomplete procedure for automatic verification of safety properties for a class of fault‐tolerant distributed protocols described in the Heard‐Of (HO for short) model. The HO model is a popular model for describing distributed protocols. We propose a verification procedure that is guaranteed to terminate even for unbounded number of the processes that execute the distributed protocol.

Author: Daniel de Leng
Publisher: Linköping University Electronic Press
ISBN: 9176850137
Category :
Languages : en
Pages : 234

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Book Description
Vast amounts of data are continually being generated by a wide variety of data producers. This data ranges from quantitative sensor observations produced by robot systems to complex unstructured human-generated texts on social media. With data being so abundant, the ability to make sense of these streams of data through reasoning is of great importance. Reasoning over streams is particularly relevant for autonomous robotic systems that operate in physical environments. They commonly observe this environment through incremental observations, gradually refining information about their surroundings. This makes robust management of streaming data and their refinement an important problem. Many contemporary approaches to stream reasoning focus on the issue of querying data streams in order to generate higher-level information by relying on well-known database approaches. Other approaches apply logic-based reasoning techniques, which rarely consider the provenance of their symbolic interpretations. In this work, we integrate techniques for logic-based stream reasoning with the adaptive generation of the state streams needed to do the reasoning over. This combination deals with both the challenge of reasoning over uncertain streaming data and the problem of robustly managing streaming data and their refinement. The main contributions of this work are (1) a logic-based temporal reasoning technique based on path checking under uncertainty that combines temporal reasoning with qualitative spatial reasoning; (2) an adaptive reconfiguration procedure for generating and maintaining a data stream required to perform spatio-temporal stream reasoning over; and (3) integration of these two techniques into a stream reasoning framework. The proposed spatio-temporal stream reasoning technique is able to reason with intertemporal spatial relations by leveraging landmarks. Adaptive state stream generation allows the framework to adapt to situations in which the set of available streaming resources changes. Management of streaming resources is formalised in the DyKnow model, which introduces a configuration life-cycle to adaptively generate state streams. The DyKnow-ROS stream reasoning framework is a concrete realisation of this model that extends the Robot Operating System (ROS). DyKnow-ROS has been deployed on the SoftBank Robotics NAO platform to demonstrate the system's capabilities in a case study on run-time adaptive reconfiguration. The results show that the proposed system - by combining reasoning over and reasoning about streams - can robustly perform stream reasoning, even when the availability of streaming resources changes.