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Novice programmers are difficult to debug on their own because of their lacking of prior knowledge. If we want to help them, first we need to able to check the correctness of a novice¡¦s program. And whenever any error is found, we could provide some suggestion to assist them in debugging. We use concolic testing algorithm to automatically generate test inputs. The test inputs generation of the concolic testing is directed by negating path conditions and is produced by solving path constraints. By using of concolic testing, we are able to explore as much more branches as we can. And once we found an error, we will try to locate it for novice programmers. We propose a new method called concolic debugging. Its idea comes from concolic testing. The concolic debugging algorithm initiates with a given failed test, and try to locate the faulty block by negating and backtracking the path conditions of the failed test. We use concolic testing to improve assessing style of the automated assessment system. 86.67% of our sample programs are successfully assessed by concolic testing algorithm on our new automated assessment system. And we also found our concolic debugging is much more stable and accuracy on fault localization then spectrum-based fault localization.

No description available.

Depuração é a atividade responsável pela localização e correção de defeitos gerados durante o desenvolvimento de programas. A depuração ocorre devido à atividade de teste bem-sucedida, na qual falhas no comportamento do programa são reveladas, indicando a existência de defeitos. Diversas técnicas têm sido propostas para automatizar a tarefa de depuração de programas. Algumas delas utilizam heurísticas baseadas em informações de cobertura obtidas da execução de testes. O objetivo é indicar trechos de código do programa mais suspeitos de conter defeitos. As informações de cobertura mais usadas em depuração automatizada são baseadas no teste estrutural de unidade. A cobertura de integração, obtida por meio da comunicação entre as unidades de um programa, pode trazer novas informações sobre o código executado, possibilitando a criação de novas estratégias para a tarefa de localização de defeitos. Este trabalho apresenta uma nova técnica de localização de defeitos chamada Depuração de programas baseada em Cobertura de Integração (DCI). São apresentadas duas coberturas de integração baseadas nas chamadas de métodos de um programa. Essas coberturas são usadas para a proposição de roteiros de busca dos defeitos a partir dos métodos considerados mais suspeitos. As informações de cobertura de unidade são então utilizadas para a localização dos defeitos dentro dos métodos. A DCI também utiliza uma nova heurística para atribuição de valores de suspeição a entidades de integração estática dos programas como pacotes, classes e métodos, fornecendo também um roteiro para a procura dos defeitos. Os experimentos realizados em programas reais mostram que a DCI permite realizar a localização de defeitos de forma mais eficaz do que o uso de informações de cobertura de unidade isoladamente. / Debugging is the activity responsible for localizing and fixing faults generated during software development. Debugging occurs due to a successful testing activity, in which failures in the behavior of the program are revealed, indicating the existence of faults. Several techniques have been proposed to automate the debugging tasks, especially the fault localization task. Some techniques use heuristics based on coverage data obtained from the execution of tests. The goal is to indicate program code excerpts more likely to contain faults. The coverage data mostly used in automated debugging is based on white-box unit testing. Integration coverage data, obtained from the communication between the units of a program, can bring about new information with respect to the executed code, which allows new strategies to the fault localization task to be devised. This work presents a new fault localization technique called Debugging based on Integration Coverage (DIC). Two integration coverages based on method invocations are presented. These coverages are used to propose two search strategies that provides a roadmap to locate faults by investigating the more suspicious methods. The unit coverage information are used to search the faulty statement inside the suspicious methods. The DIC technique also proposes a heuristic that assigns suspiciousness values to static integration entities of the programs, namely, packages, classes, and methods. This heuristic also provides a roadmap to search for the faults. Experiments using real programs show that DIC is more effective to locate faults than solely using unit coverage information.

Depuração é a atividade responsável pela localização e correção de defeitos gerados durante o desenvolvimento de programas. A depuração ocorre devido à atividade de teste bem-sucedida, na qual falhas no comportamento do programa são reveladas, indicando a existência de defeitos. Diversas técnicas têm sido propostas para automatizar a tarefa de depuração de programas. Algumas delas utilizam heurísticas baseadas em informações de cobertura obtidas da execução de testes. O objetivo é indicar trechos de código do programa mais suspeitos de conter defeitos. As informações de cobertura mais usadas em depuração automatizada são baseadas no teste estrutural de unidade. A cobertura de integração, obtida por meio da comunicação entre as unidades de um programa, pode trazer novas informações sobre o código executado, possibilitando a criação de novas estratégias para a tarefa de localização de defeitos. Este trabalho apresenta uma nova técnica de localização de defeitos chamada Depuração de programas baseada em Cobertura de Integração (DCI). São apresentadas duas coberturas de integração baseadas nas chamadas de métodos de um programa. Essas coberturas são usadas para a proposição de roteiros de busca dos defeitos a partir dos métodos considerados mais suspeitos. As informações de cobertura de unidade são então utilizadas para a localização dos defeitos dentro dos métodos. A DCI também utiliza uma nova heurística para atribuição de valores de suspeição a entidades de integração estática dos programas como pacotes, classes e métodos, fornecendo também um roteiro para a procura dos defeitos. Os experimentos realizados em programas reais mostram que a DCI permite realizar a localização de defeitos de forma mais eficaz do que o uso de informações de cobertura de unidade isoladamente. / Debugging is the activity responsible for localizing and fixing faults generated during software development. Debugging occurs due to a successful testing activity, in which failures in the behavior of the program are revealed, indicating the existence of faults. Several techniques have been proposed to automate the debugging tasks, especially the fault localization task. Some techniques use heuristics based on coverage data obtained from the execution of tests. The goal is to indicate program code excerpts more likely to contain faults. The coverage data mostly used in automated debugging is based on white-box unit testing. Integration coverage data, obtained from the communication between the units of a program, can bring about new information with respect to the executed code, which allows new strategies to the fault localization task to be devised. This work presents a new fault localization technique called Debugging based on Integration Coverage (DIC). Two integration coverages based on method invocations are presented. These coverages are used to propose two search strategies that provides a roadmap to locate faults by investigating the more suspicious methods. The unit coverage information are used to search the faulty statement inside the suspicious methods. The DIC technique also proposes a heuristic that assigns suspiciousness values to static integration entities of the programs, namely, packages, classes, and methods. This heuristic also provides a roadmap to search for the faults. Experiments using real programs show that DIC is more effective to locate faults than solely using unit coverage information.

Defects in computer programs have great social and economic impacts and should be eliminated as much as possible. Since testing and debugging are among the most costly and time consuming tasks in the software development life cycle, a variety of intelligent debugging aids have been proposed within the last three decades. Model-based software debugging (MBSD) is a particular technique that exploits discrepancies between a program execution and the intended behaviour to isolate program fragments that could potentially explain an observed misbehaviour. In contrast to other techniques, model-based debugging does not require a formal specification of a program's behaviour, making the approach suitable for developers without training in formal software engineering practices. A key aspect of model-based debugging is the transformation of the given program into a model suitable for debugging. In this thesis, several models for analysing programs written in an object-oriented language are investigated, with Java as concrete example. The aim of this work is to assess the suitability of value-based models and generalisations thereof for debugging of programs making use of dynamically allocated data structures, recursive methods and polymorphic method invocations.

Verifying the reliability and functionality of a complex system like a trading system is highly demanding since failure in such a system can cause serious economic problems. Automated random testing is a good solution to find new and rare failures in such a system. Test cases in random testing usually contain a long sequence of actions that debugging them manually to find the root cause of the failure is a very boring and tiresome task. This thesis aims to create a model for automating the task of the debugging to reduce the failed test case to an equivalent test case that only contains relevant actions that together cause the failure. Delta debugging is the core algorithm of the model that simplifies a failed test case by successive testing. The target of the project is TRADExpress system of Cinnober Financial Technology AB. The model is integrated to the random testing framework of the TRADExpress system.

Debugging is a challenging and time-consuming process in software life-cycle. The focus of the thesis is to improve the accuracy of existing fault localization (FL) techniques. We experimented with several source code line level features such as line commit size, line recency, and line length to arrive at a new fault localization technique. Based on our experiments, we propose a novel enhanced cost-aware fault localization (ECFL) technique by combining line length with the existing selected baseline fault localization techniques. ECFL improves the accuracy of DStar (Baseline 1), CombineFastestFL (Baseline 2), and CombineFL (Baseline 3) by locating 81%, 58%, and 30% more real faults respectively in Top-1 evaluation metric. In comparison with the baseline techniques, ECFL requires a marginal additional time (on an average, 5 seconds per bug) and data while providing a significant improvement in accuracy. The source code line features also improve the baseline fault localization techniques when ''learning to rank'' SVM machine learning approach is used to combine the features. We also provide an infrastructure to facilitate future research on combining new source code line features with other fault localization techniques. / Master of Science / Software debugging involves locating and fixing faults (or bugs) in software. It is a challenging and time-consuming process in software life-cycle. Fault localization (FL) techniques help software developers to locate faults by providing a ranked set of program elements. The focus of the thesis is to improve the accuracy of existing fault localization techniques. We experimented with several source code line level features such as line commit size, line recency, and line length to arrive at a new fault localization technique. Based on our experiments, we propose a novel enhanced cost-aware fault localization (ECFL) technique by combining line length with the existing selected baseline fault localization techniques. ECFL improves the accuracy of DStar (Baseline 1), CombineFastestFL (Baseline 2), and CombineFL (Baseline 3) by locating 81%, 58%, and 30% more real faults respectively in Top-1 evaluation metric. In comparison with the baseline techniques, ECFL requires a marginal additional time (on an average, 5 seconds per bug) and data while providing a significant improvement in accuracy. The source code line features also improve the baseline fault localization techniques when machine learning approach is used to combine the features. We also provide an infrastructure to facilitate future research on combining new source code line features with other fault localization techniques.

Software-defined Networking (SDN) enables flexible network management, but as networks evolve to a large number of end-points with diverse network policies, higher speed, and higher utilization, abstraction of networks by SDN makes monitoring and debugging network problems increasingly harder and challenging. While some problems impact packet processing in the data plane (e.g., congestion), some cause policy deployment failures (e.g., hardware bugs); both create inconsistency between operator intent and actual network behavior. Existing debugging tools are not sufficient to accurately detect, localize, and understand the root cause of problems observed in a large-scale networks; either they lack in-network resources (compute, memory, or/and network bandwidth) or take long time for debugging network problems. This thesis presents three debugging tools: PathDump, SwitchPointer, and Scout, and a technique for tracing packet trajectories called CherryPick. We call for a different approach to network monitoring and debugging: in contrast to implementing debugging functionality entirely in-network, we should carefully partition the debugging tasks between end-hosts and network elements. Towards this direction, we present CherryPick, PathDump, and SwitchPointer. The core of CherryPick is to cherry-pick the links that are key to representing an end-to-end path of a packet, and to embed picked linkIDs into its header on its way to destination. PathDump is an end-host based network debugger based on tracing packet trajectories, and exploits resources at the end-hosts to implement various monitoring and debugging functionalities. PathDump currently runs over a real network comprising only of commodity hardware, and yet, can support surprisingly a large class of network debugging problems with minimal in-network functionality. The key contributions of SwitchPointer is to efficiently provide network visibility to end-host based network debuggers like PathDump by using switch memory as a "directory service" - each switch, rather than storing telemetry data necessary for debugging functionalities, stores pointers to end hosts where relevant telemetry data is stored. The key design choice of thinking about memory as a directory service allows to solve performance problems that were hard or infeasible with existing designs. Finally, we present and solve a network policy fault localization problem that arises in operating policy management frameworks for a production network. We develop Scout, a fully-automated system that localizes faults in a large scale policy deployment and further pin-points the physical-level failures which are most likely cause for observed faults.