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Mainframe systems are facing a critical shortage of developer workforce as the current generation of COBOL developers retires. Furthermore, due to the limited availability of public COBOL resources, entry-level developers, who assume the mantle of legacy COBOL systems maintainers, face significant difficulties during routine maintenance tasks, such as code comprehension and defect location. While we made substantial advances in the field of software maintenance for modern programming languages yearly, mainframe maintenance has received limited attention. With this study, we aim to direct the attention of researchers and practitioners towards investigating and addressing challenges associated with mainframe development. Specifically, we explore the scope of defects affecting COBOL systems and defect location strategies commonly followed by COBOL developers and compare them with the modern programming language counterparts. To this end, we surveyed 30 COBOL and 74 modern Programming Language (PL) developers to understand the differences in defects and defect location strategies employed by the two groups. Our preliminary results show that: (1) major defect categories affecting the COBOL ecosystem are different than defects encountered in modern PL software projects; (2) the most challenging defect types in COBOL are also the ones that occur most frequently; and (3) COBOL and modern PL developers follow similar strategies to locate defective code.

We present lifestate rules--an approach for abstracting event-driven object protocols. Developing applications against event-driven software frameworks is notoriously difficult. One reason why is that to create functioning applications, developers must know about and understand the complex protocols that abstract the internal behavior of the framework. Such protocols intertwine the proper registering of callbacks to receive control from the framework with appropriate application programming interface (API) calls to delegate back to it. Lifestate rules unify lifecycle and typestate constraints in one common specification language. Our primary contribution is a model of event-driven systems from which lifestate rules can be derived. We then apply specification mining techniques to learn lifestate specifications for Android framework types. In the end, our implementation is able to find several rules that characterize actual behavior of the Android framework.

Unlike traditional programs (such as operating systems or word processors) which have large amounts of code, machine learning tasks use programs with relatively small amounts of code (written in machine learning libraries), but voluminous amounts of data. Just like developers of traditional programs debug errors in their code, developers of machine learning tasks debug and fix errors in their data. However, algorithms and tools for debugging and fixing errors in data are less common, when compared to their counterparts for detecting and fixing errors in code. In this paper, we consider classification tasks where errors in training data lead to misclassifications in test points, and propose an automated method to find the root causes of such misclassifications. Our root cause analysis is based on Pearl's theory of causation, and uses Pearl's PS (Probability of Sufficiency) as a scoring metric. Our implementation, Psi, encodes the computation of PS as a probabilistic program, and uses recent work on probabilistic programs and transformations on probabilistic programs (along with gray-box models of machine learning algorithms) to efficiently compute PS. Psi is able to identify root causes of data errors in interesting data sets.

Partial words are sequences over a finite alphabet that may contain wildcard symbols, called holes, which match or are compatible with all letters; partial words without holes are said to be full words (or simply words). Given an infinite partial word w, the number of distinct full words over the alphabet that are compatible with factors of w of length n, called subwords of w, refers to a measure of complexity of infinite partial words so-called subword complexity. This measure is of particular interest because we can construct partial words with subword complexities not achievable by full words. In this paper, we consider the notion of recurrence over infinite partial words, that is, we study whether all of the finite subwords of a given infinite partial word appear infinitely often, and we establish connections between subword complexity and recurrence in this more general framework., Comment: In Proceedings WORDS 2011, arXiv:1108.3412