A Critical Review of "Automatic Patch Generation Learned from Human-Written Patches": Essay on the Problem Statement and the Evaluation of Automatic Software Repair

At ICSE'2013, there was the first session ever dedicated to automatic program repair. In this session, Kim et al. presented PAR, a novel template-based approach for fixing Java bugs. We strongly disagree with key points of this paper. Our critical review has two goals. First, we aim at explaining why we disagree with Kim and colleagues and why the reasons behind this disagreement are important for research on automatic software repair in general. Second, we aim at contributing to the field with a clarification of the essential ideas behind automatic software repair. In particular we discuss the main evaluation criteria of automatic software repair: understandability, correctness and completeness. We show that depending on how one sets up the repair scenario, the evaluation goals may be contradictory. Eventually, we discuss the nature of fix acceptability and its relation to the notion of software correctness.Comment: ICSE 2014, India (2014

Tortoise: Interactive System Configuration Repair

System configuration languages provide powerful abstractions that simplify managing large-scale, networked systems. Thousands of organizations now use configuration languages, such as Puppet. However, specifications written in configuration languages can have bugs and the shell remains the simplest way to debug a misconfigured system. Unfortunately, it is unsafe to use the shell to fix problems when a system configuration language is in use: a fix applied from the shell may cause the system to drift from the state specified by the configuration language. Thus, despite their advantages, configuration languages force system administrators to give up the simplicity and familiarity of the shell. This paper presents a synthesis-based technique that allows administrators to use configuration languages and the shell in harmony. Administrators can fix errors using the shell and the technique automatically repairs the higher-level specification written in the configuration language. The approach (1) produces repairs that are consistent with the fix made using the shell; (2) produces repairs that are maintainable by minimizing edits made to the original specification; (3) ranks and presents multiple repairs when relevant; and (4) supports all shells the administrator may wish to use. We implement our technique for Puppet, a widely used system configuration language, and evaluate it on a suite of benchmarks under 42 repair scenarios. The top-ranked repair is selected by humans 76% of the time and the human-equivalent repair is ranked 1.31 on average.Comment: Published version in proceedings of IEEE/ACM International Conference on Automated Software Engineering (ASE) 201

Extending Provenance For Deep Diagnosis Of Distributed Systems

Diagnosing and repairing problems in complex distributed systems has always been challenging. A wide variety of problems can happen in distributed systems: routers can be misconfigured, nodes can be hacked, and the control software can have bugs. This is further complicated by the complexity and scale of today’s distributed systems. Provenance is an attractive way to diagnose faults in distributed systems, because it can track the causality from a symptom to a set of root causes. Prior work on network provenance has successfully applied provenance to distributed systems. However, they cannot explain problems beyond the presence of faulty events and offer limited help with finding repairs. In this dissertation, we extend provenance to handle diagnostics problems that require deeper investigations. We propose three different extensions: negative provenance explains not just the presence but also the absence of events (such as missing packets); meta provenance can suggest repairs by tracking causality not only for data but also for code (such as bugs in control plane programs); temporal provenance tracks causality at the temporal level and aims at diagnosing timing-related faults (such as slow requests). Compared to classical network provenance, our approach tracks richer causality at runtime and applies more sophisticated reasoning and post-processing. We apply the above techniques to software-defined networking and the border gateway protocol. Evaluations with real world traffic and topology show that our systems can diagnose and repair practical problems, and that the runtime overhead as well as the query turnarounds are reasonable

Natural Language is a Programming Language: Applying Natural Language Processing to Software Development

A powerful, but limited, way to view software is as source code alone. Treating a program as a sequence of instructions enables it to be formalized and makes it amenable to mathematical techniques such as abstract interpretation and model checking. A program consists of much more than a sequence of instructions. Developers make use of test cases, documentation, variable names, program structure, the version control repository, and more. I argue that it is time to take the blinders off of software analysis tools: tools should use all these artifacts to deduce more powerful and useful information about the program. Researchers are beginning to make progress towards this vision. This paper gives, as examples, four results that find bugs and generate code by applying natural language processing techniques to software artifacts. The four techniques use as input error messages, variable names, procedure documentation, and user questions. They use four different NLP techniques: document similarity, word semantics, parse trees, and neural networks. The initial results suggest that this is a promising avenue for future work

BugDoc: Algorithms to Debug Computational Processes

Data analysis for scientific experiments and enterprises, large-scale simulations, and machine learning tasks all entail the use of complex computational pipelines to reach quantitative and qualitative conclusions. If some of the activities in a pipeline produce erroneous outputs, the pipeline may fail to execute or produce incorrect results. Inferring the root cause(s) of such failures is challenging, usually requiring time and much human thought, while still being error-prone. We propose a new approach that makes use of iteration and provenance to automatically infer the root causes and derive succinct explanations of failures. Through a detailed experimental evaluation, we assess the cost, precision, and recall of our approach compared to the state of the art. Our experimental data and processing software is available for use, reproducibility, and enhancement.Comment: To appear in SIGMOD 2020. arXiv admin note: text overlap with arXiv:2002.0464