Despite their growing complexity and increasing size, modern software applications must satisfy strict release requirements that impose short bug fixing and maintenance cycles, putting significant pressure on developers who are responsible for timely producing high-quality software. To reduce developers workload, repairing and healing techniques have been extensively investigated as solutions for efficiently repairing and maintaining software in the last few years. In particular, repairing solutions have been able to automatically produce useful fixes for several classes of bugs that might be present in software programs. A range of algorithms, techniques, and heuristics have been integrated, experimented, and studied, producing a heterogeneous and articulated research framework where automatic repair techniques are proliferating. This paper organizes the knowledge in the area by surveying a body of 108 papers about automatic software repair techniques, illustrating the algorithms and the approaches, comparing them on representative examples, and discussing the open challenges and the empirical evidence reported so far.

Dynamic analysis of software systems produces behavioral models that are useful for analysis, verification and testing. The main techniques for extracting models of functional behavior generate either models of constraints on data, usually in the form of Boolean expressions, or models of interactions between components, usually in the form of finite state machines. Both data and interaction models are useful for analyzing and verifying different aspects of software behavior, but none of them captures the complex interplay between data values and components interactions. Thus related analysis and testing techniques can miss important information. In this paper, we focus on the generation of models of relations between data values and component interactions, and we present GK-tail, a technique to automatically generate extended finite state machines (EFSMs) from interaction traces. EFSMs model the interplay between data values and component interactions by annotating FSM edges with conditions on data values. We show that EFSMs include details that are not captured by either Boolean expressions or (classic) FSM alone, and allow for more accurate analysis and verification than separate models, even if considered jointly.

Many software components are provided with incomplete specifications and little access to the source code. Reusing such gray-box components can result in integration faults that can be difficult to diagnose and locate. In this paper, we present Behavior Capture and Test (BCT), a technique that uses dynamic analysis to automatically identify the causes of failures and locate the related faults. BCT augments dynamic analysis techniques with model-based monitoring. In this way, BCT identifies a structured set of interactions and data values that are likely related to failures (failure causes), and indicates the components and the operations that are likely responsible for failures (fault locations). BCT advances scientific knowledge in several ways. It combines classic dynamic analysis with incremental finite state generation techniques to produce dynamic models that capture complementary aspects of component interactions. It uses an effective technique to filter false positives to reduce the effort of the analysis of the produced data. It defines a strategy to extract information about likely causes of failures by automatically ranking and relating the detected anomalies so that developers can focus their attention on the faults. The effectiveness of BCT depends on the quality of the dynamic models extracted from the program. BCT is particularly effective when the test cases sample the execution space well. In this paper, we present a set of case studies that illustrate the adequacy of BCT to analyze both regression testing failures and rare field failures. The results show that BCT automatically filters out most of the false alarms and provides useful information to understand the causes of failures in 69 percent of the case studies.

Log files are commonly inspected by system administrators and developers to detect suspicious behaviors and diagnose failure causes. Since size of log files grows fast, thus making manual analysis impractical, different automatic techniques have been proposed to analyze log files. Unfortunately, accuracy and effectiveness of these techniques are often limited by the unstructured nature of logged messages and the variety of data that can be logged.This paper presents a technique to automatically analyze log files and retrieve important information to identify failure causes. The technique automatically identifies dependencies between events and values in logs corresponding to legal executions, generates models of legal behaviors and compares log files collected during failing executions with the generated models to detect anomalous event sequences that are presented to users. Experimental results show the effectiveness of the technique in supporting developers and testers to identify failure causes.

Automatic test case generation is a key ingredient of an efficient and cost-effective software verification process. In this paper we focus on testing applications that interact with the users through a GUI, and present AutoBlackTest, a technique to automatically generate test cases at the system level. AutoBlackTest uses reinforcement learning, in particular Q-Learning, to learn how to interact with the application under test and stimulate its functionalities. The empirical results show that AutoBlackTest can execute a relevant portion of the code of the application under test, and can reveal previously unknown problems by working at the system level and interacting only through the GUI.