Design implications for task-specific search utilities for retrieval and re-engineering of code

Abstract

The importance of information retrieval systems is unquestionable in the modern society and both individuals as well as enterprises recognise the benefits of being able to find information effectively. Current code focused information retrieval systems such as Google Code Search, Codeplex or Koders produce results based on specific keywords and therefore they do not take into account developers' context such as development language, technology/framework, goal of the project, project complexity and maturity or developer's domain expertise. They also impose additional cognitive burden on users in switching between different interfaces and clicking through to find the relevant code. Hence, they are not used by software developers. In this paper, we discuss how software engineers interact with information and general purpose information retrieval systems (e.g. Google, Yahoo) and investigate to what extent domain-specific search and recommendation utilities can be developed in order to support their work related activities. Based on our user studies, we found that software engineers followed many identifiable and repeatable work tasks and behaviours. These behaviours can be used to develop implicit relevance feedback based systems. Based on our results, we discuss the implications for the development of task-specific search and collaborative recommendation utilities embedded with the Google standard search engine and Microsoft IntelliSense for retrieval and reengineering of code. We have implemented a prototype of the proposed collaborative recommendation system which was evaluated in a controlled environment simulating the real world situation of professional software engineers. The evaluation has achieved promising initial results on the precision and recall performance of the system.

TEMPORARY REMOVAL: Big Data analytics: Computational intelligence techniques and application areas

“…Surveillance cameras are used to monitor traffic flow, threats, and suspicious activities in order to support government and lawenforcement agencies. They play a vital role in security issues, as they are widely deployed in public places to prevent or investigate crimes and to detect individuals acting suspiciously, in order to make the environment more secure (Iqbal, Grzywaczewski et al, 2015;Iqbal, Odetayo et al, 2015). Computational intelligence methods can help to classify physiological and behavioural traits to effectively identity suspicious and abnormal behaviour for various applications such as vehicle theft, crowd surveillance through monitoring and analysis of multiple real-time data streams.…”

“…In order to capitalize on the advantages of Big Data analytics in an increasingly knowledge driven society there is a need to develop solutions that reduce the complexity and cognitive burden on accessing and processing these large volumes of data in both embedded hardware and software-based data analytics (Maniak, Jayne, Iqbal, & Doctor, 2015;Iqbal, Grzywaczewski, Halloran, Doctor, & Iqbal, 2015;Iqbal, Odetayo et al, 2015). Big challenges stem from the utilization of Big Data in the real world, since the implementation of real time applications is becoming increasingly complex.…”

Big data analytics: Computational intelligence techniques and application areas

“…Surveillance cameras are used to monitor traffic flow, threats, and suspicious activities in order to support government and law-enforcement agencies. They play a vital role in security issues, as they are widely deployed in public places to prevent or investigate crimes and to detect a suspicious person in order to make the environment more secure (Iqbal et al, 2015a, Iqbal et al, 2015b. Computational intelligence methods can help to classify physiological and behavioural traits to effectively identity suspicious and abnormal behaviour for various applications such as vehicle theft, crowd surveillance through monitoring and analysis of multiple real-time data streams.…”

“…In order to harvest the advantages of Big Data analytics in an increasingly knowledge driven society there is a need to develop solutions that reduce the complexity and cognitive burden on accessing and processing these large volumes of data in both embedded hardware and software based data analytics (Maniak et al, 2015;Iqbal et al, 2015a, Iqbal et al, 2015b. Big challenges stem from the utilization of Big Data in real world, since the implementation of real time applications is becoming increasingly complex.…”

Factors Affecting Users‟ Measure of Interest: A Study of the Effect of Task, Document Difficulty and Document Familiarity

“…Multi-purpose personalisation approaches have been developed over the years but this has been limited to laboratory use. Research [19,20] has shown that such generic personalisation approach does not adequately capture users" interest in a real-life situation. To create an efficient and robust system that will personalise user interaction with the web, contextual approaches need to be employed to capture users" information seeking behaviour and incorporate it into the system.…”