Hypothesis-based concept assignment to support software maintenance

Software comprehension is one of the most expensive activities in software maintenance and many tools have been developed to help the maintainer reduce the time and cost of the task. Of the numerous tools and methods available, one group has received relatively little attention: those using plausible reasoning to address the concept assignment problem. This problem is defined as the process of assigning descriptive terms to their implementation in source code, the terms being nominated by a user and usually relating to computational intent. It has two major research issues: Segmentation: finding the location and extent of concepts in the source code.Concept Binding', determining which concepts are implemented at these locations. This thesis presents a new concept assignment method: Hypothesis-Based Concept Assignment (HB-CA). A framework for the activity of software comprehension is defined using elements of psychological theory and software tools. In this context, HB-CA is presented as a successful concept assignment method for COBOL II, employing a simple knowledge base (the library) to model concepts, source code indicators, and inter-concept relationships. The library and source code are used to generate hypotheses on which segmentation and concept binding are performed. A two-part evaluation is presented using a prototype implementation of HB-CA. The first part shows that HB-CA has linear computational growth in the length of program under analysis. Other characteristics addressed include HB-CA's scalability, its applicability to other languages, the contribution made by different information sources, domain independence, representational power, and guidelines for the content of the library. The first part concludes by comparing the method and implementation to cognitive requirements for software comprehension tools. The second part considers applications of HB-CA in software maintenance. Five areas for potential cost reduction are identified: business-rule ripple analysis, code ripple analysis, module selection, software reuse, and software module comprehension

Analysis reuse exploiting taxonomical information and belief assignment in industrial problem solving

To take into account the experience feedback on solving complex problems in business is deemed as a way to improve the quality of products and processes. Only a few academic works, however, are concerned with the representation and the instrumentation of experience feedback systems. We propose, in this paper, a model of experiences and mechanisms to use these experiences. More specifically, we wish to encourage the reuse of already performed expert analysis to propose a priori analysis in the solving of a new problem. The proposal is based on a representation in the context of the experience of using a conceptual marker and an explicit representation of the analysis incorporating expert opinions and the fusion of these opinions. The experience feedback models and inference mechanisms are integrated in a commercial support tool for problem solving methodologies. The results obtained to this point have already led to the definition of the role of ‘‘Rex Manager’’ with principles of sustainable management for continuous improvement of industrial processes in companies

Code extraction algorithms which unify slicing and concept assignment

One approach to reverse engineering is to partially automate subcomponent extraction, improvement and subsequent recombination. Two previously proposed automated techniques for supporting this activity are slicing and concept assignment. However, neither is directly applicable in isolation; slicing criteria (sets of program variables) are simply too low level in many cases, while concept assignment typically fails to produce executable subcomponents. This paper introduces a unification of slicing and concept assignment which exploits their combined advantages, while overcoming their individual weaknesses. Our 'concept slices' are extracted using high level criteria, while producing executable subprograms. The paper introduces three ways of combining slicing, and concept assignment and algorithms for each. The application of the concept slicing algorithms is illustrated with a case study from a large financial organisation

Continuous Improvement Through Knowledge-Guided Analysis in Experience Feedback

Continuous improvement in industrial processes is increasingly a key element of competitiveness for industrial systems. The management of experience feedback in this framework is designed to build, analyze and facilitate the knowledge sharing among problem solving practitioners of an organization in order to improve processes and products achievement. During Problem Solving Processes, the intellectual investment of experts is often considerable and the opportunities for expert knowledge exploitation are numerous: decision making, problem solving under uncertainty, and expert configuration. In this paper, our contribution relates to the structuring of a cognitive experience feedback framework, which allows a flexible exploitation of expert knowledge during Problem Solving Processes and a reuse such collected experience. To that purpose, the proposed approach uses the general principles of root cause analysis for identifying the root causes of problems or events, the conceptual graphs formalism for the semantic conceptualization of the domain vocabulary and the Transferable Belief Model for the fusion of information from different sources. The underlying formal reasoning mechanisms (logic-based semantics) in conceptual graphs enable intelligent information retrieval for the effective exploitation of lessons learned from past projects. An example will illustrate the application of the proposed approach of experience feedback processes formalization in the transport industry sector

Data-Driven Application Maintenance: Views from the Trenches

In this paper we present our experience during design, development, and pilot deployments of a data-driven machine learning based application maintenance solution. We implemented a proof of concept to address a spectrum of interrelated problems encountered in application maintenance projects including duplicate incident ticket identification, assignee recommendation, theme mining, and mapping of incidents to business processes. In the context of IT services, these problems are frequently encountered, yet there is a gap in bringing automation and optimization. Despite long-standing research around mining and analysis of software repositories, such research outputs are not adopted well in practice due to the constraints these solutions impose on the users. We discuss need for designing pragmatic solutions with low barriers to adoption and addressing right level of complexity of problems with respect to underlying business constraints and nature of data.Comment: Earlier version of paper appearing in proceedings of the 4th International Workshop on Software Engineering Research and Industrial Practice (SER&IP), IEEE Press, pp. 48-54, 201